جستجوی مقالات مرتبط با کلیدواژه « خوشه بندی » در نشریات گروه « کتابداری و مدیریت اطلاعات »

Considering the importance of brand positioning for any business and the growing trend of research in this field, the necessity of conducting scientific studies is fully felt. A review of the subject literature and domestic and international research shows that no comprehensive study has been done on this issue. Considering the extent and influence of the field of brand positioning in most business research, the need for clarification is evident to reveal the current situation and future trends based on scientific techniques. To fill this existing deficiency, the present research seeks to answer the question: What is the scientific map of published studies in the field of brand positioning in the Scopus database? Based on this mapping, the study aims to analyze the network of concepts in this field and identify the most valuable areas. It addresses the topics within this field and assists researchers in brand positioning to explain the study process and policy-making in this field based on the identified influential areas.

The present research is a quantitative, applied, descriptive, and scientometric study based on the objective. The research employed co-word analysis and network analysis techniques. The statistical population comprised all scientific and specialized articles on brand positioning indexed in the Scopus citation database from 1975 to 2023. Initially, the term "brand positioning" was searched in the titles, abstracts, and keywords of the Scopus database. Subsequently, the search was refined to include only articles and reviews, followed by a selection of English language articles. Finally, 577 scientific and specialized articles on brand positioning were chosen for analysis. VOSviewer and Gephi software were utilized for visualizing and analyzing scientific maps of the 577 selected documents. The Scopus database was chosen due to its vast collection of 70 million reviewed research articles from over 5000 international publishers, ensuring relative uniformity.

The research findings reveal that marketing, branding, brand image, brand management, and consumer behavior are the most common topics and keywords in the realm of brand positioning. According to the generated maps and utilizing the closeness centrality index, branding, competition, marketing, brand image, product design, and brand management emerge as the most crucial topics in information dissemination within the network. In essence, they represent categories with an interdisciplinary perspective in the domain of brand positioning.

The present study is the first systematic quantitative analysis of brand positioning research in the Scopus citation database, which uses co-word analysis to map the scientific landscape of this field. This study addresses the gap in scientometric research in the literature, aiming to enhance the understanding of brand positioning research from its inception to the present. Research questions were formulated for this purpose and will be addressed in this article. This analysis can serve as a guide for future research in brand positioning. The results indicate an increasing trend in research over the years. The main topics identified in brand positioning research include branding, brand performance, brand social responsibility, destination branding, brand positioning, and brand origin. Through co-citation analysis, key authors in the brand positioning research community were identified, such as Keller, Aaker, Diamantopoulos, and Hayer. Given the significance of green branding (a primary keyword in the corporate social responsibility cluster), the rise in environmental risks, and the impact of green branding on brand sustainability, companies in our country should prioritize this area when establishing and implementing their brand.

The purpose of this research is to determine the pattern of access to information resources and analysis of loan transactions among users of public libraries in Ilam province using the approach of association rules, clustering and classification in data mining.

In this research, by using data mining techniques, and algorithms of convergence, clustering, and classification rules on data related to the circulation desk of public libraries in Ilam province, the demographic information affecting the study behavior of members and the patterns of using information resources by this heterogeneous community were extracted. The data mining process used in the research (CRISP-DM) consists of six pahses, including business understanding, data understanding, data preparation, modeling (rules of convergence, artificial neural network, and decision tree), evaluation, and deployment, respectively.

The findings related to different groups using information sources showed that in addition to studying their textbooks, students also studied subjects such as Iranian history and Islamic religion. In terms of gender, women with bachelorʼs degrees were interested in studying Persian language and literature, and fiction. The analysis of resource lending in terms of gender showed that 58 percent of active members of public libraries in Ilam province were women and 42 percent of them were men. This difference shows that women have a greater desire to use the information resources of public libraries in Ilam province. Furthermore, among those who were late in returning their borrowed books, 54 percent were women and 46 percent were men; In terms of returning books on time, women showed their responsibility with 52 percent and men with 48 percent. The results show that men with diplomas were interested in issues related to Islam and this group felt more responsibility for returning their books. In terms of gender and its effect on the studied subjects, undergraduate women, who had free jobs, showed interest in the classes of Persian literature and fiction and did not delay in returning their books. The analysis of the behavior model of the patrons in terms of book return revealed that the men with diploma who had free jobs did not delay in returning loaned books, and the women with diploma and bachelorʼs degrees who had free jobs did not delay in returning loaned books. Moreover, students with undergraduate degrees delayed in returning loaned books. A neural network model was developed to predict the delay in the return of books based on characteristics such as gender and educational level, and the accuracy rate of the model during the learning process with the best prediction accuracy was equal to 69.18%. This shows that it is possible to learn and predict the output by receiving new entries in the database of public libraries of Ilam province.

Originality/value: 

The deployment of the proposed neural network model on the database of public libraries of Ilam province for predicting the delay in the return of books based on different characteristics, shows that it is possible to learn and predict the output by receiving new entries in the database of these libraries. Moreover, using the association rules technique, it is possible to design a book recommendation system in public libraries of Ilam province.

Due to the importance of artificial intelligence, the increasing growth, and the high penetration rate of this technology in other sciences, the necessity of conducting scientometric studies is fully felt. The literature review on the research topic shows that very few studies have been conducted in this case or with not enough comprehensiveness. Considering rapid and significant progress in this field, it needs to be clarified to reveal the current situation and future trends based on scientometric techniques.To fill this existing gap, the present study seeks to answer the question that how is the scientific map of the published studies in the field of artificial intelligence by Iranian researchers in the Scopus database, and based on this map, what are the noteworthy fields of researchers?

The current research is a quantitative and practical study using scientific methods, including co-word analysis and social network analysis. The research population includes all documents in the field of artificial intelligence with at least one author who is affiliated with an Iranian organization and indexed in the Scopus database from the beginning to January 2022. VOSviewer and Gephi are the software used to analyze data and extract maps. The reason for choosing the Scopus database is that this database contains 70 million research articles reviewed by more than 5000 international publishers (Zhang et. al, 2021), and also the database has relative uniformity in all fields (Batooli et al. 2019).

The results of the analysis show that Iranian researchers have published the most documents in 2021, and also the best position of Iran in the field of artificial intelligence was the 20th rank, which was achieved in 2021. Overall, Iran ranks 31 in thisdatabase and third among Middle Eastern countries after Israel and Turkey.Among the types of published documents, articles with number of 3124 are in the first place and notes are in the last place with a number of two published items.19.9% of the published documents of Iranian papers have not received any citations. The greatest number of published documents have between 1 and 5 citations. The highest number of citations belongs to an article with 824 citations.Also, findings show the most frequent keywords are "artificial intelligence", "algorithm" and "optimization".The network structure of high-frequency keywords with a repetition threshold of at least 15, consists of five clusters. In the first cluster, the keyword"neural network" has the highest link weight. In the next "artificial intelligence", "machine learning" and "learning systems" have the highest link weights and the only word in the fifth cluster is "mathematical model" and keywords are placed in five clusters: “inference and prediction”, “intelligent algorithms”, “diagnosis and learning”, “data analysis”, and “mathematical models”.In the coauthor network of 80 countries collaborating with Iranian writers, the most collaboration is between Iranian and American researchers. This network has classified collaborating countries into eight clusters based on direct relations.

The findings showed that the most frequent keywords are artificial intelligence, algorithm, optimization, and neural network. Optimizing is common between this study and GhaviDel, Nezamdost, & Riahinia's (2020). Also, the neural network is common between the present study and the study of Feng & Law (2021) and the study of Darko et al. (2020).The comparison of the most frequent keywords in Iranian and non-Iranian documents shows that Iranian researchers in choosing their research topics were aligned to the general trend of scientific development in most years, and in some years, they have progressed with a step delay.The results of the cluster analysis show that the studies were divided into five thematic clusters. This is even though the study of GhaviDel, Nezamdost, & Riahinia (2020) identified 12 clusters. Also, the difference between the results of the clustering of the present study and the study of Taherian & Assareh (2012) is that in the present study, clustering was done on the documents published by Iranian researchers, but in that study, the clustering was done on all the published documents in the field of artificial intelligence.According to the findings of the research, at the beginning of 2022, Iran was in the 31st rank.Meanwhile, Iran's rank in the study of FarzinYazdi and RezaeiSharifabadi (2017) in the same Scopus database was 17.Iranian researchers do not observe standard affiliations of education institutes and it leads to inaccuracy in the search results.Also, the results show that the process of Iranian documents has been generally upward. However, this growth rate is lower compared to other countries. In the following research topics, Iranian researchers have been in line with the general direction for most of the years, and in some years they have been one step behind.

Today, data generation through smart equipment, including mobile phones, has faced a significant growth, and clustering is one of the most widely used knowledge discovery techniques in big data. Density-based clustering (DBSCAN) is one of the most efficient clustering algorithms in data mining, and despite having advantages, it also has problems, such as the difficulty in determining the input parameters, as well as not being able to detect clusters. with different densities. In the proposed algorithm of this article, it is inspired by the K-DBSCAN algorithm in grouping large data with the aim of reducing the clustering execution time.In addition, by using K-Means and H-DBSCAN algorithms, different densities of the data set were identified and an Eps radius was determined for each density, and then, the proposed density-based clustering algorithm was developed with parameters The matching is applied to the data, and in fact, the innovation of this article is the use of K Means clustering and the estimation of different densities in the DBSCAN clustering method. The proposed algorithm has been compared with the simple DBSCAN clustering algorithm and two developed K-DBSCAN and H-DBSCAN algorithms on four standard data sets: Image segmentation, Pendigit, Letters and Shuttle control. The results show that the proposed algorithm is superior to other algorithms when both time and accuracy are criteria in clustering.

The accumulated volume of customer information due to the growth and development of information technology and the creation of databases has led companies that want to provide better services to their customers to benefit from new tools for customer relationship. One of these tools and methods is data mining techniques that can play an important and key role in customer relationship management. The purpose of this study is to analyze customer value with a combined data mining approach based on the WRFM model.  So 64858 samples from customer database in the period 2019-2020 have been selected by available purposive sampling method. The weight of WRFM attributes has been determined by surveying 3 experts of the company using a hierarchical analysis process. Based on the initial variables of the research and the variables obtained from the attributes of the WRFM model, the purchase value of customers has been analyzed. SPSS Modeller and SPSS software were used to analyze the data.The results show that the K-Means clustering method has a better performance in customer segmentation than the TwoStep clustering and the Cohonen neural network methods. Finally, based on the criteria of purity percentage, repetition, error rate and Normalized Mutual Information (NMI (index, six clusters with NMI (0.631) were selected from different K-Means clustering.This study introduces the WRFM model for customer value analysis.The weight of the attributes of this model is based on a survey of experts and using a hierarchical analysis process based on the degree of incompatibility (0.052) obtained from the hierarchical analysis method (0.15), (0.29) and (0.56), respectively, have been determined that these values ​​indicate the greater importance of the monetary value index than the other two indices; Finally, these six clusters were divided into 4 general categories using naming market segments methods in research (Chang and Tsai 2004; Babaian and Sarfarazi 2019): key and special customers, golden potential customers, missing uncertain customers and new uncertain customers. According to the research model, the company should focus more on its specific and key customers, ie customers who are in the first, third and fifth clusters, ie loyal customers who have higher than average values in the two attributes of monetary value and frequency and recently they have had purchases with a high value of Rials that the company should consider effective marketing strategies for this group of customers due to its limited resources in order to lead to more profitability for the company while maintaining customer relationship management.

Students’ growth and development have always been considered important by the education system as they are the future assets of their country. Although many students can flourish their talents and creativity, we are faced with a large number of students each year whose talents are squandered leading them to a position far from success. Today, with the more students entering in different academic levels and the variety of study disciplines, the necessity to properly guide students is felt more than ever. For this purpose, educational data mining has received special attention from the educational system’s officials in recent years. So far, various classification methods and techniques in data mining and machine learning have been used to predict student performance. However, these individual classifiers have limitations such as complexity and instability for predicting performance in the education process. To tackle this problem, ensemble classification has been proposed as a new and efficient method. Ensemble classification systems combine the results of several individual classifiers to provide a model with better performance. In this paper, a new Ensemble classification system is presented using multilayer neural networks and SOM clustering in order to estimate and classify the grade point average of undergraduate students. In addition, we used averaging and majority voting as combination methods for aggregating the results of individual classifiers. Evaluation results on real university data show that our proposed ensemble system provides better accuracy and performance compared to prevalent individual classification methods. Also, the proposed ensemble system obviously outperforms other popular ensemble methods in classifying students’ GPA.

One of the ways to detect spam is classifying emails into two categories: spam and non-spam. The high efficiency of machine learning methods in various fields has developed them in text clasification problems. The mechanism of machine learning-based classifiers that classify emails according to their content is based on a set of features, where due to the high volume of emails, using an efficient feature reduction algorithm plays an important role. Unlike the previous methods which select only the superior features and ignore the rest of the unselected features, in the proposed method of this article we try to use unselected features as well. The method is that after applying an initial feature selection, the unselected features are clustered and then each cluster is mapped to a new feature and the final feature vector forms from the selected ones and those mapped from the clusters. In this study, by applying two methods of selecting the initial feature and also two mapping functions, four methods were presented and analyzed using two datasets PU2 and PU3. The results of the analysis showed that the method based on feature selection DF and the advanced mapping function has the highest efficiency among all the proposed methods. Also, the proposed methods are more efficient than base feature selection methods (without clustering).

The purpose is to present an open government data evaluation method by considering comprehensive and complete dimensions and criteria - calculating the weight and importance of each criterion, examining the country in this area, clustering organizations and presenting a classification model to predict the situation.

Library studies was used to extract the dimensions and criteria of evaluation. Population includes articles related to open government data evaluation criteria. Ten articles were reviewed by simple random sampling method. Multiple attribute decision making techniques was used to calculate the weight and importance of each criterion. Data mining techniques was incorporated to cluster and create a classification model.

By reviewing the articles 15 criteria of open government data evaluation including:  Data originality, license openness, up-to-datedness, data access rate, metadata completeness, number of data sets, format openness, non-discriminatory, comprehensible, number of data fields, free, no missing data, data request ability, visual and feedback, were extracted. Using AHP technique, the weights of the criteria were calculated, which after normalization, the total weight of the 15 extracted criteria was equal to one. "Data originality" with a weight of 0.165, " license openness " with a weight of 0.124 and " up-to-datedness" with a weight of 0.109 were ranked first to third among 15 evaluation criteria, respectively. Weight of evaluation criteria obtained and data extraction of 358 organizations in harmony with 15 evaluation criteria, the weight of organizations was calculated. The sum of the weights was equal to one. "East Azerbaijan Agricultural Jihad Organization" with a weight of 0.088, "Statistics Center of Iran" with a weight of 0.062 and "Geological Survey" with a weight of 0.058 were the first to third ranks among 358 organizations and government institutions, respectively, based on the combination of criteria and the weight of criteria.

Evaluation criteria obtained, calculating the weight and importance of each criterion, examining the current situation of government organizations and institutions in the country and the classification model created can help managers to understand the current situation and improve it and thus increase citizens' interaction with open government data as a kind of human information interaction.

The main purpose of this article is to analyze the data of the Tehran Municipality websites and provide data mining solutions for managers' decisions.

This research is fundamental and in terms of nature, it can be considered analytical. The data collection method was the field. The statistical population was selected from 220 domains of Tehran Municipality and for analysis, data mining techniques were used to discover the appropriate decision model of city managers. The source of data collection was web analytics and tools used by Google Analytics.

The accuracy of the LSTM deep neural network is 99.84%. Network accuracy is 99.90%, the call is equal to 99.63%, the error is equal to 0.16%, MSM standard is equal to 0.003. The accuracy of the DBScan method with other basic methods for analyzing the data of Tehran Municipality websites is 99.84%, the deep learning method is 99.25%, the nearest neighbor method is 99.81% and the decision tree method is equal to 99.8%.With these interpretations, the rate of improvement of the accuracy of the DBScan method in comparison with the deep learning methods is equal to 0.59%.

Finally, by simulating the DBScan method to identify and analyze the data of Tehran Municipality websites and provide data mining solutions for managers' decisions, it was observed that the proposed method provides suggestions to managers to improve site visits and The performance of the municipality is significantly effective.

Clustering as a process to understand the nature and structure of data plays an important role in organizing data in many areas of science and technology. One of the most widely used and simple algorithms for clustering is K-means. The present study was conducted to systematically reviewing research on improving K-means algorithm on data clustering. This research examines the researches conducted in this field and its role in organizing data in the range of 2010 to 2020 with a new strategy based on the shortcomings of the K-means algorithm. For this purpose, the amount of attention of researchers to eliminate any of the shortcomings of this algorithm in order to improve it in recent years has been compiled in the form of research questions. In this study, with the use of a search strategy for refining and extracting articles, 47 related sources were identified and examined. Findings showed that most researches have been done by overcoming the sensitive shortcomings to initial cluster centers to improve the K-means algorithm. Also, out of a total of 47 studies, the improved K-means algorithm has been applied in 35 studies on non-textual data and in 12 studies on textual data. Finally, the results of a review of six studies showed that the amount of data is directly related to the performance of improved K-means algorithm. In other words, this algorithm must be modified in such a way as to perform efficient and accurate clustering by applying it to different amounts of data.

This study aimed to investigate the knowledge map in obstetrics and gynecology research using network analysis and visualization approaches through co-word analysis.

This applied research has been conducted with a scientometrics approach using the co-word analysis. The study population consisted of 57769 records on obstetrics and gynecology indexed in the Web of Science Database during 2014-2018.

The keywords "Pregnancy" and "Pregnancy-Preeclampsia" have been shown to have the highest rate of frequency and co-occurrence frequency in obstetrics and gynecology research, respectively. Hierarchical clustering leads to the formation of seven clusters, Clusters of "Caesarean", "Preeclampsia" and "Women's Cancer" are considered as adult clusters and "infertility" cluster is not a central cluster, but it is developed. It was also found that the clusters of "pre-natal screening", "menopause" and "pregnancy complications" are among the emerging or declining clusters.

Conclustion: 

The knowledge structure of obstetrics and gynecology revealed the status of research in this field which could be a guide for future researches. In the field of obstetrics and gynecology, issues related to infertility and its psychological problems need more attention from researchers.

How to implement business process in many organizations, especially government agencies, is described through documentation in the form of announcements, guidelines and circulars. Increasing the quality of process documentation makes it possible to avoid individual intentions, confusion and deliberate or unwanted inappropriate errors, and thus result in a better business process performance. Despite the importance of document quality in business process performance, very few models are available to determine the desirable level of documentation quality indicators and literature focuses more on defining information quality indicators. In response to this need in the public administration, in this paper, a model is proposed to determine the level of desirable quality of information indicators of documentation in business processes in accordance with process characteristics. This model is based on data collected from fifty-one processes in government agencies that have appropriate qualitative documentation. Collected data was clustered into five groups of homogeneous processes using one-time clustering based on “process characteristics” and one-time based on “process characteristics” and “information quality indicators of each process's documentation” together. Then the pattern in the “information quality indicators of process's documentation” is identified in each group.

A word is the smallest unit in the language that has 'form' and 'meaning'. The word might have more than one meaning in which its exact meaning is determined according to the context it is appeared. Collecting all words’ senses manually is a tedious and time consuming task. Moreover, it is possible that the words’ meanings change over time such that the meaning of an existing word will become unusable or a new meaning will be added to the word. Computational methods is one of the approaches used for identifying words’ senses with respect to the linguistic contexts.In this paper, we put an effort to propose an algorithm to identify senses of Persian words automatically without a human supervision. To reach this goal, we utilize the word embedding method in a vector space model. To build words’ vectors, we use an algorithm based on the neural network approach to gather the context information of the words in the vectors. In the proposed model of this research, the divisive clustering algorithm as one of hierarchical clustering algorithms fits with the requirements of our research question. In the proposed model, two modes, namely the Sentence-based and the Context-based, are introduced to identify words’ senses. In the Sentence-based mode, all of the words in a sentence that contain the target word are involved to build the sentence vector; while in the Context-based mode, only a limited number of surrounding words of the target word is involved to build the sentence vector. Two evaluation methods, namely internal and external, are required to evaluate the performance of the clustering algorithm. The silhouette score for each cluster is computed as the internal evaluation metric for both modes of the proposed model. The external evaluation requires a gold standard data for which a data set containing 20 ambiguous words and 100 sentences for each target word is developed.According to the obtained results of the internal evaluation, the Sentence-based mode has higher density of clusters than the Context-based mode, and the difference between them is statistically significant. According to the V- and F-measure evaluation metrics in the external evaluation, the Context-based mode has obtained higher performance against the baselines with statistically significant difference.

This study has been done to explore the Intellectual Structure of the Positive Psychology studies. Library Studding is the approach of present research, in theoretical literature, and application in terms of the nature, and analytical in analysis of findings, and method and finally clustering have used, those are text mining techniques to explore the Intellectual Structure and relationships the topics of positive psychology studies. The populations studied in this research are the 1086 papers that are published in positive psychology topics around the world, since 2000 to 2012, and are indexed in the Scopus database. After extraction of related terms from the titles, abstracts and keywords of articles and applying text mining techniques and words stemming, a co-word matrix was created with dimensions of 43 × 43. For the interpretation of relationship major and minor categories, this matrix was standard and based on the similarity was interred to XL.STAT Software. The terms were optimized in 7 clusters. For the next steps of analysis has been used from MATHLAB software, and for display coword network from Pajak software. The results of this research show that Cluster 2 by 10, and Cluster 4 by 9 terms, are known as the principal clusters. The Cluster 5 has the most of selective terms from the major categories and Cluster 2 & 1 have minimum distance that it shows the relationship between the major and minor topics in these clusters is higher.

urpose: This research has investigated and demonstrated the scientific structure and knowledge map of the researches of wisdom.

the study used scientometrics approach. The statistical population of this research consisted from all scientific documents registered at the Scopus. In this research, scientific documents were extracted from Scopus and their co-word analysis was done after standardization. For each subject area and the keywords, a column diagram was formed, and then neighboring and co-neighboring graphs were performed.

The findings showed that the fields of social sciences, computer science, business, management and accounting, economics, econometrics and finance, environmental sciences, engineering sciences, mathematics and decision-making sciences have done the most research work related to the study of wisdom. The most common words were wisdom, knowledge, ethics, education, leadership, technology, personality, and business. It is also proposed to develop the knowledge map of wisdom research, the main structures of "wise leadership," "living with universal culture," "teaching Practical wisdom" and "wisdom-based creativity ".

The results showed that the concept of wisdom with regard to large capacities, especially in Iran, was not considered for the basic needs of society.

With the increasing growth of Web-based resources and articles, the use of quick and inexpensive ways to access the texts is important from the vast collection of these documents. The main objective of this research is to cluster the base of Iranian Research Institute for information Science and Technology (IranDoc) based on text mining techniques. So that the articles are ivided into several clusters so that the articles of the different clusters have the maximum possible difference and the articles in each cluster have the most similarity. Articles on information technology related fields were selected. For this purpose, first all the keywords of information technology fields were selected based on their frequencies in base articles and then the articles of each keyword were extracted from the Iran Doc database. Then, using the notepad ++ software, the dataset was created. In this research, clustering of k_means algorithm and Euclidean distance function criterion were used to measure the similarity of clusters. Then the results of the clustering were analyzed to find the similarity and pattern among the papers. The pattern showed that the greatest similarity is found between articles in the two data mining clusters and the neural network with an Euclidean distance of 1.365, and the least similarity between the two cluster articles is optimization and image processing with a distance of 1.387. Research knowledge, clustering of articles related to the highest and the least degree of similarity with each other, finding a new pattern for quick and easy access to similar articles, and discovering hidden relationships among different subjects. This knowledge helps researchers to access topic-related articles related to specialization Identify themselves and the subject of the study in a more desirable way.

Aim: This study aims to assess the information seeking behavior of seminary students using neural network in Khorasan-e- Razavi.
Research The study is an applied and descriptive survey conducted with quantitative approach using neural networks.
Data were collected by a questionnaire included with the information seeking behavior of seminary students in Khorasan-e- Razavi province distributed between the samples.
After collecting data, the neural network was selected for clustering the data and using Matlab 14 software, seminary students were clustered individually and as a whole on the basis of the main components of the.
Then, after removing each sub-component of the main components of the research, the most effective and the least effective option on information seeking behavior of seminary students were determined.
Findings: Considering clustering using self-organizing neural networks, the most effective component in motivation was determined as conducting research, cultural and religious activities. The least effective component were determined as academic reputation and compete with peers.
The most important component of information seeking skill was awareness of their information needs, but the least component was not observed.
The most effective way to access information in the seminary schools was print resources and the least effective way was determined as buying needed resources.
According to the aim and findings of this study, it is possible to assess the information behavior of seminary students, using neural networks method and clustering sampling.

The aim of the current research is the analysis of patent classes to recognize the subject areas of RFID technology. The research is a descriptive one and uses the clustering and the social network analysis techniques for data analysis. The research population consisted of 35, 627 patents that the term “RFID” or “Radio Frequency Identification” occurred in their abstracts or titles. Data analysis was performed through softwares
like Excel, SPSS, NetDraw, and RavarMatrix. The findings showed that the growth of patents in the intended technology followed an exponential regression. The important subclasses were determined by two factors: number of years and frequency of use. It resulted in 803 important subclasses. The result of clustering for main classes also revealed that the RFID technology was associated with 31 subject areas. The axes or runts of these areas were Telecommunication and Electrical Communication.
Social Network Analysis confirmed that the “Electrical Communication” have-had the most significant role for bridging other classes.

Nowadays, one of the most important things that make companies pay attention to the customer satisfaction is the competition in the world and companies struggle to achieve sustainable competitive advantage and strategic superiority over their competitors. The aims of this research is to identify the customers’ requirement in Nyazco online
shopping and cluster these customers based on two methods, k-means algorithm and fuzzy Kano model, in order to offer products according to each customer’s needs. In this study, 1090 records of customers were examined for data mining of these customers in the period of 7 months, and four clusters of customers were defined. To measure the customers’ satisfactions, the fuzzy Kano questionnaire was designed and randomly
assigned to 168 customers, who were selected based on Cochran formula, and finally, the requirements of each cluster were investigated and classified by using fuzzy Kano model. The results of this research show that the category of some characteristics is different between some clusters and is the same between other clusters. Also, the satisfaction and dissatisfaction score is calculated for each cluster. The results of this
study indicate that the customers of the third cluster are more important for this site because the transaction frequencies of these customers and the total amount of purchasing are high. The result of this study will help other online shopping centers for etter presentation of goods to different customers.

The present study classifies target users of Central Library of Isfahan to identify their behaviors and information needs. The research uses applied and descriptive-survey method with quantitative approach using neural network techniques in data-mining. The research tool was a researcher-made questionnaire including 64 items with the average
reliability of Cronbach’s alpha coefficient 0.862. The sample population consisted of 356 faculty members and graduate students of Isfahan University of Technology in the year 1393-94. Using MATLAB, they were clustered based on each of the main components of the study (the purpose and motivation of information-seeking, the amount of using information sources, information-seeking obstacles and skills, and the amount of using
information services). Removing each of the research’s sub-components of the main components demonstrated the highest purpose and motivation of information-seeking among the target population, and the least effective were reaching a scientific level and competition with rivals; the information sources mostly needed by library users were translations of Latin books, and their minimum requirements were reports and research schemes; the main barrier to their information-seeking was lack of access to proper
Internet and the least effective factor in lack of access to information sources was inconvenient use of the sources; the skill of awareness about the required information and access to information channel are most efficient items in the information seeking skills. The most inefficient item is the ability of users in recognizing the importance of obtained information from their searches with respect to their required information; the most effective services in meeting the information needs of the users were the ability of librarians and information science experts in information retrieval and their knowledge of information services and resources, and the least effective were familiarity with a variety of information services and resources in their professional major and possibility of using the information sources in the form of digital files needless of attending libraries. According to the purpose and results of the present study, it is possible to classify target users of libraries via using neural network technique in data-mining, based on their needs and information-seeking behaviors.