{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T22:38:17Z","timestamp":1776465497631,"version":"3.51.2"},"reference-count":0,"publisher":"IGI Global","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,10,1]]},"abstract":"<p>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.<\/p>","DOI":"10.4018\/ijdwm.2020100104","type":"journal-article","created":{"date-parts":[[2020,10,23]],"date-time":"2020-10-23T14:39:31Z","timestamp":1603463971000},"page":"63-83","source":"Crossref","is-referenced-by-count":3,"title":["Discovering Similarity Across Heterogeneous Features"],"prefix":"10.4018","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0130-6135","authenticated-orcid":true,"given":"Vandana P.","family":"Janeja","sequence":"first","affiliation":[{"name":"University of Maryland, Baltimore County, USA"}]},{"given":"Josephine M.","family":"Namayanja","sequence":"additional","affiliation":[{"name":"University of Massachusetts, Boston, USA"}]},{"given":"Yelena","family":"Yesha","sequence":"additional","affiliation":[{"name":"University of Maryland Baltimore County, USA"}]},{"given":"Anuja","family":"Kench","sequence":"additional","affiliation":[{"name":"University of Maryland, Baltimore County, USA"}]},{"given":"Vasundhara","family":"Misal","sequence":"additional","affiliation":[{"name":"University of Maryland, Baltimore County, USA"}]}],"member":"2432","container-title":["International Journal of Data Warehousing and Mining"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=265257","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T23:47:09Z","timestamp":1651880829000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJDWM.2020100104"}},"subtitle":["A Case Study of Clinico-Genomic Analysis"],"short-title":[],"issued":{"date-parts":[[2020,10,1]]},"references-count":0,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,10]]}},"URL":"https:\/\/doi.org\/10.4018\/ijdwm.2020100104","relation":{},"ISSN":["1548-3924","1548-3932"],"issn-type":[{"value":"1548-3924","type":"print"},{"value":"1548-3932","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,1]]}}}