Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Alternatives to the k-means algorithm that find better clusterings
Proceedings of the eleventh international conference on Information and knowledge management
Topic-Based Hard Clustering of Documents Using Generative Models
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
An empirical study of data smoothing methods for memory-based and hybrid collaborative filtering
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Data clustering with size constraints
Knowledge-Based Systems
Producing accurate interpretable clusters from high-dimensional data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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Recently published studies have shown that partitional clustering algorithms that optimize certain criterion functions, which measure key aspects of inter- and intra-cluster similarity, are very effective in producing hard clustering solutions for document datasets and outperform traditional partitional and agglomerative algorithms. In this paper we study the extent to which these criterion functions can be modified to include soft membership functions and whether or not the resulting soft clustering algorithms can further improve the clustering solutions. Specifically, we focus on four of these hard criterion functions, derive their soft-clustering extensions, and present an experimental evaluation involving twelve different datasets. Our results show that introducing softness into the criterion functions tends to lead to better clustering results for most datasets.