Selecting a clustering algorithm is a perplexing task. Yet since different algorithms may yield dramatically different outputs on the same data, the choice of algorithm is crucial. When selecting a clustering algorithm, users tend to focus on cost-related considerations (software purchasing costs, running times, etc). Differences concerning the output of the algorithms are not usually considere...

A formal framework for the treatment of hierarchical coalition formation and hierarchical agreements under both the bargaining and blocking approaches to coalition formation is introduced, and some first positive results on the possibility of full agreement and the efficiency of hierarchical agreements in face of externalities are given. In particular, it is shown that the possibility of hierar...

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ions for Community-Based Administration

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 da...

The hierarchy is often used to infer knowledge from groups of items and relations in varying granularities. Hierarchical clustering algorithms take an input of pairwise data-item similarities and output a hierarchy of the data-items. This paper presents Bidirectional agglomerative hierarchical clustering to create a hierarchy bottom-up, by iteratively merging the closest pair of data-items into...

We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including t...

Clustering techniques find interesting and previously unknown patterns in large scale data embedded in a large multi dimensional space and are applied to a wide variety of problems like customer segmentation, Biology, data mining techniques, machine Learning and geographical information systems. Clustering algorithms are used efficiently to scale up with the dimensionality of the data sets and ...

one of the most important issues in urban planning is developing sustainable public transportation. the basic condition for this purpose is analyzing current condition especially based on data. data mining is a set of new techniques that are beyond statistical data analyzing. clustering techniques is a subset of it that one of it’s techniques used for analyzing passengers’ trip. the result of t...