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

Vandana P. Janeja, Josephine M. Namayanja, Yelena Yesha, Anuja Kench and Vasundhara Misal
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Vandana P. Janeja: University of Maryland, Baltimore County, USA
Josephine M. Namayanja: University of Massachusetts, Boston, USA
Yelena Yesha: University of Maryland Baltimore County, USA
Anuja Kench: University of Maryland, Baltimore County, USA
Vasundhara Misal: University of Maryland, Baltimore County, USA

International Journal of Data Warehousing and Mining (IJDWM), 2020, vol. 16, issue 4, 63-83

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jdwm00:v:16:y:2020:i:4:p:63-83

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