Abstract
This paper discusses a density based clustering approach for a guided kernel based clustering algorithm, named MK-means (Modified K-means). Our idea is to improve the guided K-Means clustering algorithm and discuss the benefits of using MK-Means algorithm for clustering algorithm in astrophysics data bases. The improvements made allow handling clustering without apriori knowledge and also include the flexibility of merging classes when similarities are detected.
Original language | Unknown |
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Title of host publication | IEEE International Joint Conference on Neural Networks (IJCNN) |
Pages | 1-6 |
DOIs | |
Publication status | Published - 1 Jan 2010 |
Event | IEEE World Congress on Computational Intelligence (WCCI 2010) - Duration: 1 Jan 2010 → … |
Conference
Conference | IEEE World Congress on Computational Intelligence (WCCI 2010) |
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Period | 1/01/10 → … |