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Modified Single Pass Clustering Algorithm Based on Median as a Threshold Similarity Value

Modified Single Pass Clustering Algorithm Based on Median as a Threshold Similarity Value

Mamta Mittal, R. K. Sharma, V.P. Singh, Lalit Mohan Goyal
Copyright: © 2017 |Pages: 25
ISBN13: 9781522504894|ISBN10: 1522504893|EISBN13: 9781522504900
DOI: 10.4018/978-1-5225-0489-4.ch002
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MLA

Mittal, Mamta, et al. "Modified Single Pass Clustering Algorithm Based on Median as a Threshold Similarity Value." Collaborative Filtering Using Data Mining and Analysis, edited by Vishal Bhatnagar, IGI Global, 2017, pp. 24-48. https://doi.org/10.4018/978-1-5225-0489-4.ch002

APA

Mittal, M., Sharma, R. K., Singh, V., & Mohan Goyal, L. (2017). Modified Single Pass Clustering Algorithm Based on Median as a Threshold Similarity Value. In V. Bhatnagar (Ed.), Collaborative Filtering Using Data Mining and Analysis (pp. 24-48). IGI Global. https://doi.org/10.4018/978-1-5225-0489-4.ch002

Chicago

Mittal, Mamta, et al. "Modified Single Pass Clustering Algorithm Based on Median as a Threshold Similarity Value." In Collaborative Filtering Using Data Mining and Analysis, edited by Vishal Bhatnagar, 24-48. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0489-4.ch002

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Abstract

Clustering is one of the data mining techniques that investigates these data resources for hidden patterns. Many clustering algorithms are available in literature. This chapter emphasizes on partitioning based methods and is an attempt towards developing clustering algorithms that can efficiently detect clusters. In partitioning based methods, k-means and single pass clustering are popular clustering algorithms but they have several limitations. To overcome the limitations of these algorithms, a Modified Single Pass Clustering (MSPC) algorithm has been proposed in this work. It revolves around the proposition of a threshold similarity value. This is not a user defined parameter; instead, it is a function of data objects left to be clustered. In our experiments, this threshold similarity value is taken as median of the paired distance of all data objects left to be clustered. To assess the performance of MSPC algorithm, five experiments for k-means, SPC and MSPC algorithms have been carried out on artificial and real datasets.

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