Algorithms for clustering data
Algorithms for clustering data
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Clustering validity checking methods: part II
ACM SIGMOD Record
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Word sense disambiguation in information retrieval revisited
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Selecting the "right" number of senses based on clustering criterion functions
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Posters & Demonstrations
An evaluation of criteria for measuring the quality of clusters
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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The goal of any clustering algorithm producing flat partitions of data is to find the optimal clustering solution and the optimal number of clusters. One natural way to reach this goal without the need for parameters, is to involve a validity index in the clustering process, which can lead to an objective selection of the optimal number of clusters. In this paper, we provide two main contributions. Firstly, since validity indices have been mostly studied in small dimensional datasets, we have chosen to evaluate them in a real-world task: agglomerative clustering of words. Secondly, we propose a new context-aware method that aims at enhancing the validity indices usage as stopping criteria in agglomerative algorithms. Experimental results show that the method is a step-forward in using, with more reliability, validity indices as stopping criteria.