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Adaptive Threshold Based Clustering: A Deterministic Partitioning Approach

Adaptive Threshold Based Clustering: A Deterministic Partitioning Approach

Mamta Mittal, Rajendra Kumar Sharma, Varinder Pal Singh, Raghvendra Kumar
Copyright: © 2019 |Volume: 10 |Issue: 1 |Pages: 18
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781522566601|DOI: 10.4018/IJISMD.2019010103
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MLA

Mittal, Mamta, et al. "Adaptive Threshold Based Clustering: A Deterministic Partitioning Approach." IJISMD vol.10, no.1 2019: pp.42-59. http://doi.org/10.4018/IJISMD.2019010103

APA

Mittal, M., Sharma, R. K., Singh, V. P., & Kumar, R. (2019). Adaptive Threshold Based Clustering: A Deterministic Partitioning Approach. International Journal of Information System Modeling and Design (IJISMD), 10(1), 42-59. http://doi.org/10.4018/IJISMD.2019010103

Chicago

Mittal, Mamta, et al. "Adaptive Threshold Based Clustering: A Deterministic Partitioning Approach," International Journal of Information System Modeling and Design (IJISMD) 10, no.1: 42-59. http://doi.org/10.4018/IJISMD.2019010103

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Abstract

Partitioning-based clustering methods have various challenges especially user-defined parameters and sensitivity to initial seed selections. K-means is most popular partitioning based method while it is sensitive to outlier, generate non-overlap cluster and non-deterministic in nature due to its sensitivity to initial seed selection. These limitations are regarded as promising research directions. In this study, a deterministic approach which do not requires user defined parameters during clustering; can generate overlapped and non-overlapped clusters and detect outliers has been proposed. Here, a minimum support value has been adopted from association rule mining to improve the clustering results. Further, the improved approach has been analysed on artificial and real datasets. The results demonstrated that datasets are well clustered with this approach too and it achieved success to generate almost same number of clusters as present in real datasets.

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