نتایج جستجو برای: clustering analysis
تعداد نتایج: 2864801 فیلتر نتایج به سال:
the ways of placing decision making units (dmus) in certain clusters are found as a subject in statistics, these ways usually are heuristic. the proposed clustering approach in this article considers preferences of dmus. this study applies data envelopment analysis (dea) dmus are clustered by solving multi-objective linear problem (molp) and by considering preferences of each dmu at production ...
The lack of complete coverage of hydrological data forces hydrologists to use the homogenization methods in regional analysis. In this research, in order to choose the best Hierarchical clustering method for regional analysis, base flow and related index were extracted from daily stream flow data using two parameter recursive digital filters in 43 hydrometric stations of the Kerman province. Ph...
In the real world clustering problems, it is often encountered to perform cluster analysis on data sets with mixed numeric and categorical values. However, most existing clustering algorithms are only efficient for the numeric data rather than the mixed data set. In addition, traditional methods, for example, the K-means algorithm, usually ask the user to provide the number of clusters. In this...
In the election of a hierarchical clustering method, theoretic properties may give some insight to determine which method is the most suitable to treat a clustering problem. Herein, we study some basic properties of two hierarchical clustering methods: α-unchaining single linkage or SL(α) and a modified version of this one, SL∗(α). We compare the results with the properties satisfied by the cla...
The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...
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 analys...
this paper explores the empirical evidence of the nature of intra-metropolitan supply linkages and industrial clustering and searches for the driving forces that enhances the learning processes and innovation capacities hence; contributing to competitive advantage within the tehran metropolitan. the research points to accelerating growth in the automotive sector since the late 1980s and early 1...
Cluster analysis is a useful technique in multivariate statistical analysis. Different types of hierarchical cluster analysis and K-means have been used for data analysis in previous studies. However, the K-means algorithm can be improved using some metaheuristics algorithms. In this study, we propose simulated annealing based algorithm for K-means in the clustering analysis which we refer it a...
This work draws its inspiration from three important sources of research on dissimilarity-based clustering and intertwines those three threads into a consistent principled functorial theory of clustering. Those three are the overlapping clustering of Jardine and Sibson, the functorial approach of Carlsson and Mémoli to partition-based clustering, and the Isbell/Dress school’s study of injective...
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