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Discovering Similarity Across Heterogeneous Features: A Case Study of Clinico-Genomic Analysis

Discovering Similarity Across Heterogeneous Features: A Case Study of Clinico-Genomic Analysis

Vandana P. Janeja, Josephine M. Namayanja, Yelena Yesha, Anuja Kench, Vasundhara Misal
Copyright: © 2020 |Volume: 16 |Issue: 4 |Pages: 21
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781799805007|DOI: 10.4018/IJDWM.2020100104
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MLA

Janeja, Vandana P., et al. "Discovering Similarity Across Heterogeneous Features: A Case Study of Clinico-Genomic Analysis." IJDWM vol.16, no.4 2020: pp.63-83. http://doi.org/10.4018/IJDWM.2020100104

APA

Janeja, V. P., Namayanja, J. M., Yesha, Y., Kench, A., & Misal, V. (2020). Discovering Similarity Across Heterogeneous Features: A Case Study of Clinico-Genomic Analysis. International Journal of Data Warehousing and Mining (IJDWM), 16(4), 63-83. http://doi.org/10.4018/IJDWM.2020100104

Chicago

Janeja, Vandana P., et al. "Discovering Similarity Across Heterogeneous Features: A Case Study of Clinico-Genomic Analysis," International Journal of Data Warehousing and Mining (IJDWM) 16, no.4: 63-83. http://doi.org/10.4018/IJDWM.2020100104

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Abstract

The analysis of both continuous and categorical attributes generating a heterogeneous mix of attributes poses challenges in data clustering. Traditional clustering techniques like k-means clustering work well when applied to small homogeneous datasets. However, as the data size becomes large, it becomes increasingly difficult to find meaningful and well-formed clusters. In this paper, the authors propose an approach that utilizes a combined similarity function, which looks at similarity across numeric and categorical features and employs this function in a clustering algorithm to identify similarity between data objects. The findings indicate that the proposed approach handles heterogeneous data better by forming well-separated clusters.

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