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Most clustering algorithms produce a single clustering for a given data set even when the data can be clustered naturally in multiple ways. In this paper, we address the difficult...
Clustering stability is an increasingly popular family of methods for performing model selection in data clustering. The basic idea is that the chosen model should be stable under...
Tags lack organizational structure limiting their utility for navigation. We present two clustering algorithms that improve this by organizing tags automatically. We apply the alg...
The goal of any clustering algorithm is to find the optimal clustering solution with the optimal number of clusters. In order to evaluate a clustering solution, a number of validit...
Despite outstanding successes of the state-of-the-art clustering algorithms, many of them still suffer from shortcomings. Mainly, these algorithms do not capture coherency and homo...
Domain experts are frequently interested to analyze multiple related spatial datasets. This capability is important for change analysis and contrast mining. In this paper, a novel ...
Abstract. This paper is about the evaluation of the results of clustering algorithms, and the comparison of such algorithms. We propose a new method based on the enrichment of a se...
In this paper, we propose a fast, memory-efficient, and scalable clustering algorithm for analyzing transactional data. Our approach has three unique features. First, we use the c...
Abstract. We propose a new clustering algorithm satisfying requirements for the post-clustering algorithms as many as possible. The proposed “Fuzzy Concept ART” is the form of ...
Short texts clustering is one of the most difficult tasks in natural language processing due to the low frequencies of the document terms. We are interested in analysing these kind...
Diego Ingaramo, David Pinto, Paolo Rosso, Marcelo ...