means algorithm
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In this paper, instead of the classical approach to the multi-criteria location selection problem, a new approach was presented based on selecting a portfolio of locations. First, the indices affecting the selection of maintenance stations were collected. The K-means model was used for clustering the maintenance stations. The optimal number of clusters was calculated through the Silhouette index. The efficiency of each cluster of stations was determined using the Charnes, Cooper and Rhodes input-oriented data envelopment analysis model. A bi-objective zero one programming model was used to select a Pareto optimal combination of rank and distance of stations. The Pareto solutions for the presented bi-objective model were determined using the invasive weed optimization method. Although the proposed methodology is meant for the selection of repair and maintenance stations in an oil refinery Company, it can be used in multi-criteria decision-making problems.
Keywords: Facility location problem, DEA, CCR . K, means algorithm, Invasive weedoptimization, Multiple, criteria decision analysis -
The growth of AVL (Automatic Vehicle Location) systems leads to huge amount of data about different parts of bus fleet (buses, stations, passenger, etc.) which is very useful to improve bus fleet efficiency. In addition, by processing fleet and passengers’ historical data it is possible to detect passenger’s behavioral patterns in different parts of the day and to use it in order to improve fleet plans. In this research, a new approach is developed to use AVL data to investigate relationship between headway change and passenger downfall rate. For this purpose, a new method is developed that is called Intelligent Headway Selection (IHS) approach. The aim of this approach is finding similar days from passengers’ behavior perspective in the dataset and by focusing on unusual patterns of each group, headway changes effects on passenger downfall rate is being studied. In this approach, in the first step, each day is classified into specific time periods (like half of hours) and the passengers’ behavior pattern is detected for each day during the specified time periods. Then, in the K-Means algorithm, Euclidian distance measure is replaced with Dynamic Time Warping (DTW) algorithm to enable the K-Means to compare time series. The modified K-Means algorithm is used to compare days in the dataset and categorize similar days in the same clusters. Then, headway – passenger per minute plot is created for each time period to detect unusual patterns. Then, a Headway Interval Detection Procedure (HIDP) is developed to use these unusual patterns to find suitable headway values for each time period. Afterwards, these plots merged and the final headways are calculated.Keywords: Headway, AVL, Dynamic Time Warping (DTW), Data mining, k, means algorithm, Bus scheduling
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Uncertain and stochastic states have been always taken into consideration in the fields of risk management and accident, like other fields of industrial engineering, and have made decision making difficult and complicated for managers in corrective action selection and control measure approach. In this research, huge data sets of the accidents of a manufacturing and industrial unit have been studied by applying clustering methods and association rules as data mining methods. First, the accident data was briefly studied. Then, effective features in an accident were selected while consulting with industry experts and considering production process information. By performing clustering method, data was divided into separate clusters and by using Dunn Index as validator of clustering, optimum number of clusters has been determined. In the next stage, by using the Apriori Algorithm as one of association rule methods, the relations between these fields were identified and the association rules among them were extracted and analyzed. Since managers need precise information for decision making, data mining methods, when to be used properly, may act as a supporting system.Keywords: Accident, data mining, association rules, K, means algorithm, a priori Algorithm
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در سال های اخیر اهمیت داده ها به عنوان منابع دارای پتانسیل اطلاعاتی بسیار بالا به نحو گسترده یی مورد توجه قرار گرفته شده است. داده کاوی با استخراج و کشف سریع و دقیق اطلاعات با ارزش و پنهان از پایگاه داده ها به منظور تصمیم گیری و پشتیبانی تصمیم از جمله اموری است که هر کشور، سازمان و شرکتی به منظور توسعه علمی، فنی و اقتصادی خود به آن نیاز دارد. با توجه به ضرورت استفاده از فنون داده کاوی ٓخصوصا خوشه بندیٓ در این نوشتار یک مدل ریاضی براساس رویکرد خوشه بندی ارائه می شود که در خوشه بندی مشتریان شرکت صنعتی پارس خزر کاربرد دارد. مسئله ی خوشه بندی به صورت مدل ریاضی با هدف کمینه سازی مجموع متوسط فواصل درون خوشه یی در طبقه بندی مشتریان فرمول بندی می شود که در تمامی موارد آزمایش شده، با بهبودبخشی شدید در فواصل درون خوشه یی همراه است. عملکرد این شیوه در یک مسئله ی واقعی آزموده شده و تحلیل نتایج حاکی از کارایی محاسبات این شیوه است.
کلید واژگان: داده کاوی، خوشه بندی، روش های خوشه بندی سلسله یی و غیرسلسله یی، مدیریت ارتباط با مشتری، الگوریتم k، m e a n sIn recent years, data importance is widely considered as a resource with high information potential. Data mining is a process of extracting and refining knowledge from a large database. The extracted information can be used to predict, classify, model, and characterize the data being mined. It is an intelligent method of discovering unknown or unexplored relationships within a large database. It uses the principles of pattern recognition and machine learning to discover knowledge, and various statistical and visualization techniques to present the knowledge in a comprehensible form. Data mining with extraction, and rapid and precise discovery of valuable and hidden information from data bases, is used for decision making and decision support. It is a technique that every country, organization and company requires in order for scientific, technological and economic development. Nowadays, considering the strong competition condition of companies and organizations to gain new customers and maintain previous customers, the volume of customer information and dramatically complex interaction with customers, data mining has been a pioneer for acquisition profitability in customer relationships, considering the necessity to use data mining, especially clustering. In this paper, the first concept of clustering and its applications is considered, then a review about data mining mathematical concepts and clustering, including: minimizing the sums of squares within clustering, components related to p-dimension Euclidean space, minimizing the distances of the mean squared sum within clustering, number of clusters, minimizing the total distance within clustering, and minimizing the maximum distance within clusters, is addressed. A model of ``minimizing the mean sum of distances within clusters in customer segmentation'' is proposed. The proposed model is formulated as a mathematical model with the objective of minimizing the mean sum of distances within clusters. Then, the model is compared with the ``minimizing the distances mean squared sum within clustering'' model using MATLAB 7.5.0 (R2007b). The proposed method does not depend on any initial positions for the cluster centers and does not allow any empirically adjustable parameters. In the tested cases, the model has improved the distances within clusters. Finally, the performance of the model is tested on a real problem for classification of the Parskhazar Industry customers. Experiments reveal that the proposed model has efficient yield.Keywords: Data mining, clustering, hierarchical and nonhierarchical clustering, customer relationship management, k, means algorithm -
مساله خوشه بندی به منظور کمینه کردن مجموع مجذور انحراف، یک مساله غیر خطی و غیر محدب بوده و دارای تعداد زیادی نقاط بهینه محلی است. هدف از این مقاله، ارائه روشی ترکیبی با استفاده از الگوریتم ژنتیک و K-Means برای خروج از نقاط بهینه محلی است.استفاده از الگوریتم ژنتیک برای خروج از نقاط بهینه محلی، توسط محققین بسیاری انجام شده است. در این مقاله روش های جدیدی برای عملگرهای بازترکیبی و جهش ارائه شده است. منطق روش های پیشنهادی بر این امر استوار است که اگر عملگرهای تغییر به جای آنکه بطور تصادفی در کل فضای جواب اعمال گردند، در یک منطقه محدود از پیش تعریف شده، انجام شوند، به جواب های بهتری دست خواهیم یافت. برای ارزیابی الگوریتم پیشنهادی، از سه نوع عملگر جهش و پنج نوع عملگر بازترکیبی بر روی مجموعه داده های استاندارد استفاده شده است. مقایسه نتایج بدست آمده با سایر روش ها، به ازای Kهای متفاوت، نشان می دهد می توان با استفاده از عملگر بازترکیبی ساده یک نقطه ای و عملگر جهش ارائه شده در این مقاله با نام «عملگر جهش منطقه ای خوشه ای»، به جواب های بهتری دست یافت.
کلید واژگان: خوشه بندی، الگوریتم ژنتیک، الگوریتم K، MeansInternational Journal of Industrial Engineering & Production Management, Volume:23 Issue: 1, 2012, PP 121 -128The clustering problem under the criterion of minimum sum of squares is a non-convex and non-linear program, which possesses many locally optimal values, resulting that its solution often being stuck at locally optimal values and therefore cannot converge to global optima solution. In this paper, we introduce several new variation operators for the proposed hybrid genetic algorithm for the clustering problem. The novel mutation operator, called Clustering Regional Mutation, exchanges neighboring centers and a simple one-point crossover. The proposed algorithm identifies proper clustering. The experimental results are given to illustrate the effectiveness of the new genetic algorithm.Keywords: Clustering, Genetic algorithm, K, Means algorithm
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