hierarchical clustering

The aim of this research is to design a Total Productive Maintenance (TPM) management model using a hybrid method of artificial neural networks and hierarchical clustering in power distribution companies of northwestern Iran. This study is conducted in power distribution companies of northwestern Iran, which were selected as pilot companies. Determining the optimal maintenance strategy and selecting the best management model for maintenance is of great importance. The findings of this study will be provided to Tavanir and the Ministry of Energy for further implementation in other subsidiary companies. In terms of location, the quantitative data pertains to operational data of the power distribution companies in northwestern Iran. The statistical population includes experts and personnel from the maintenance, repair, and warehousing departments of these companies. The temporal data pertains to operational data from the inventory, accounting, and process systems of power distribution companies in northwestern Iran, spanning from 2017 to 2022. The results of this research indicate that initiating the Total Productive Maintenance process requires strong managerial leadership. Subsequently, processes should be improved and undergo initial feedback evaluations. By considering the strength of human resources and enhancing employee skills, the quality of work processes will be analyzed. As these factors evolve, the system will undergo precise organization and planning. Comprehensive preventive maintenance will ensure workplace safety and health are prioritized. Another aspect that management must address is the advancement of technology and the expansion of automation systems, especially in the implementation of equipment and inventory management subsystems and resource and contract management, which are key priorities of the model. Finally, management must focus on adopting preventive maintenance, self-controlled maintenance, and re-evaluating current practices. Employees should be engaged in achieving these three goals.

With the development of electrical network infrastructure and the emergence of concepts such as demand response and using electric vehicles for purposes other than transportation, knowing the behavioral patterns of network technical specifications to manage electrical systems has become very important optimally. One of the critical parameters in the electrical system management is the distribution network imbalance. There are several ways to improve and control network imbalances. One of these ways is to detect the behavior of bus imbalance profiles in the network using data analysis. In the past, data analysis was performed for large environments such as states and countries. However, after the emergence of smart grids, behavioral study and recognition of these patterns in small-scale environments has found a fundamental and essential role in the deep management of these networks. One of the appropriate methods in identifying behavioral patterns is data mining. This paper uses the concepts of hierarchical and k-means clustering methods to identify the behavioral pattern of the imbalance index in an unbalanced distribution network. For this purpose, first, in an unbalanced network without the electric vehicle parking, the imbalance profile for all busses is estimated. Then, by applying the penetration coefficient of 25% and 75% for electric vehicles in the network, charging\discharging effects on the imbalance profile is determined. Then, by determining the target cluster and using demand response, the imbalance index is improved. This method reduces the number of busses competing in demand response programs. Next, using the concept of classification, a decision tree is constructed to minimize metering time.

Today we live in a period that is known to an area of communication. By increasing the information on the internet, the extra news are published on news agencies websites or other resources, the users are confused more with the problems of finding their desired information and related news. Among these are recommended systems they can automatically finding the news and information of their favorite’s users and suggesting to them too. This article attempts to improve the user’s interests and user’s satisfactions by refining the content based recommendation system to suggest better sources to their users. A clustering approach has been used to carry out this improvement. An attempt has been made to define a cluster threshold for clustering the same news and information in the K-means clustering algorithm. By detecting best resemblance criterion value and using an external knowledge base (ontology), we could generalize words into a set of related words (instead of using them alone). This approach is promoted the accuracy of news clustering and use the provided cluster to find user’s favorite news and also could have suggest the news to the user. Since the dataset has an important and influential role in advisory recommended systems, the standard Persian dataset is not provided and not published yet. In this research, an attempted has been made to connect and publish the dataset to finish the effect of this vacuum. The data are collected and crawl 8 periods of days from the Tabnak news agency website. The profile of each volunteers has been created and also saved at the same time as they read the favorite news on that period of time. An analysis shows that the proposed clustering approach provided by the NMI criterion has reached 70.2%  on our the dataset. Also, using the suggested clustering recommendation system yield 89.2% performance based on the accuracy criterion, which shows an improvement of 8.5% in a standardized way.

In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the data points as one-dimensional ink drop patterns, in order to summarize the effects of all data points, and then applies a threshold on the resulting vectors. It is based on an ensemble clustering method which performs one-dimensional density partitioning to produce ensemble of clustering solutions. Then, it assigns a unique prime number to the data points that exist in each partition as their labels. Consequently, a combination is performed by multiplying the labels of every data point in order to produce the absolute labels. The data points with identical absolute labels are fallen into the same cluster. The hierarchical property of the algorithm is intended to cluster complex data by zooming in each already formed cluster to find further sub-clusters. The algorithm is verified using several synthetic and real-world datasets. The results show that the proposed method has a promising performance, compared to some well-known high-dimensional data clustering algorithms.

Traditional leveraging statistical methods for analyzing today’s large volumes of spatial data have high computational burdens. To eliminate the deficiency, relatively modern data mining techniques have been recently applied in different spatial analysis tasks with the purpose of autonomous knowledge extraction from high-volume spatial data. Fortunately, geospatial data is considered a proper subject for leveraging data mining techniques. The main purpose of this paper is presenting a hybrid geospatial data clustering mechanism in order to achieve a high performance hotspot analysis method. The method basically works on 2 or 3-dimensional geographic coordinates of different natural and unnatural phenomena. It uses the systematic cooperation of two popular clustering algorithms: the AGlomerative NEStive, as a hierarchical clustering method and κ-means, as a partitional clustering method. It is claimed that the hybrid method will inherit the low time complexity of the κ-means algorithm and also relative independency from user’s knowledge of the AGNES algorithm. Thus, the proposed method is expected to be faster than AGNES algorithm and also more accurate than κ-means algorithm. Finally, the method was evaluated against two popular clustering measurement criteria. The first clustering evaluation criterion is adapted from Fisher’s separability criterion, and the second one is the popular minimum total distance measure. Results of evaluation reveal that the proposed hybrid method results in an acceptable performance. It has a desirable time complexity and also enjoys a higher cluster quality than its parents (AGNES and κ-means). Real-time processing of hotspots requires an efficient approach with low time complexity. So, the problem of time complexity has been taken into account in designing the proposed approach.