In this paper, we propose a parameter-insensitive data partitioning approach for Chameleon, a hierarchical clustering algorithm. The proposed method splits a given dataset into every possible number of clusters by using existing algorithms that do allow arbitrary-sized sub-clusters in partitioning. After that, it evaluates the quality of every set of initial sub-clusters by using our measurement function, and decides the optimal set of initial sub-clusters such that they show the highest value of measurement. Finally, it merges these optimal initial sub-clusters repeatedly and produces the final clustering result. We perform extensive experiments, and the results show that the proposed approach is insensitive to parameters and also produces a set of final clusters whose quality is better than the previous one.

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Garg AKapil NShukla S(2021)Prediction of the Target User Segment for Paid Advertisement2021 IEEE 8th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)10.1109/UPCON52273.2021.9667580(1-5)Online publication date: 11-Nov-2021
https://doi.org/10.1109/UPCON52273.2021.9667580

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A data partitioning approach for hierarchical clustering

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ICUIMC '13: Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication

January 2013

772 pages

ISBN:9781450319584

DOI:10.1145/2448556

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Published: 17 January 2013

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Hanyang University

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ICUIMC '13: The 7th International Conference on Ubiquitous Information Management and Communication

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Garg AKapil NShukla S(2021)Prediction of the Target User Segment for Paid Advertisement2021 IEEE 8th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)10.1109/UPCON52273.2021.9667580(1-5)Online publication date: 11-Nov-2021
https://doi.org/10.1109/UPCON52273.2021.9667580

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