An Evidence Accumulation Approach to Constrained Clustering Combination
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
A Novel Path-Based Clustering Algorithm Using Multi-dimensional Scaling
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Minimum spanning tree based split-and-merge: A hierarchical clustering method
Information Sciences: an International Journal
Fast parameterless density-based clustering via random projections
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Data clustering is a long standing research problem in pattern recognition, computer vision, machine learning, and data mining with applications in a number of diverse disciplines. The goal is to partition a set of n d-dimensional points into k clusters, where k may or may not be known. Most clustering techniques require the definition of a similarity measure between patterns, which is not easy to specify in the absence of any prior knowledge about cluster shapes. While a large number of clustering algorithms exist, there is no optimal algorithm. Each clustering algorithm imposes a specific structure on the data and has its own approach for estimating the number of clusters. No single algorithm can adequately handle various cluster shapes and structures that are encountered in practice. Instead of spending our effort in devising yet another clustering algorithm, there is a need to build upon the existing published techniques. In this talk we will address the following problems: (i) clustering via evidence accumulation, (ii) simultaneous clustering and dimensionality reduction, (iii) clustering under pair-wise constraints, and (iv) clustering with relevance feedback. Experimental results show that these approaches are promising in identifying arbitrary shaped clusters in multidimensional data.