An initialization method for clustering mixed numeric and categorical data based on the density and distance

Jinchao Ji, Wei Pang, Yanlin Zheng, Zhe Wang, Zhiqiang Ma

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Most of the initialization approaches are dedicated to the partitional clustering algorithms which process categorical or numerical data only. However, in real-world applications, data objects with both numeric and categorical features are ubiquitous. The coexistence of both categorical and numerical attributes make the initialization methods designed for single-type data inapplicable to mixed-type data. Furthermore, to the best of our knowledge, in the existing partitional clustering algorithms designed for mixed-type data, the initial cluster centers are determined randomly. In this paper, we propose a novel initialization method for mixed data clustering. In the proposed method, both the distance and density are exploited together to determine initial cluster centers. The performance of the proposed method is demonstrated by a series of experiments on three real-world datasets in comparison with that of traditional initialization methods.
Original languageEnglish
Article number1550024
Number of pages16
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume29
Issue number7
Early online date28 Jul 2015
DOIs
Publication statusPublished - Nov 2015

Fingerprint

Clustering algorithms
Experiments

Keywords

  • clustering
  • data mining
  • mixed numeric and categorical data
  • cluster center initialization

Cite this

An initialization method for clustering mixed numeric and categorical data based on the density and distance. / Ji, Jinchao; Pang, Wei; Zheng, Yanlin; Wang, Zhe; Ma, Zhiqiang.

In: International Journal of Pattern Recognition and Artificial Intelligence, Vol. 29, No. 7, 1550024, 11.2015.

Research output: Contribution to journalArticle

@article{57020795ce864539b1f48cdd744c3188,
title = "An initialization method for clustering mixed numeric and categorical data based on the density and distance",
abstract = "Most of the initialization approaches are dedicated to the partitional clustering algorithms which process categorical or numerical data only. However, in real-world applications, data objects with both numeric and categorical features are ubiquitous. The coexistence of both categorical and numerical attributes make the initialization methods designed for single-type data inapplicable to mixed-type data. Furthermore, to the best of our knowledge, in the existing partitional clustering algorithms designed for mixed-type data, the initial cluster centers are determined randomly. In this paper, we propose a novel initialization method for mixed data clustering. In the proposed method, both the distance and density are exploited together to determine initial cluster centers. The performance of the proposed method is demonstrated by a series of experiments on three real-world datasets in comparison with that of traditional initialization methods.",
keywords = "clustering, data mining, mixed numeric and categorical data, cluster center initialization",
author = "Jinchao Ji and Wei Pang and Yanlin Zheng and Zhe Wang and Zhiqiang Ma",
year = "2015",
month = "11",
doi = "10.1142/S021800141550024X",
language = "English",
volume = "29",
journal = "International Journal of Pattern Recognition and Artificial Intelligence",
issn = "0218-0014",
publisher = "World Scientific Publishing Co. Pte Ltd",
number = "7",

}

TY - JOUR

T1 - An initialization method for clustering mixed numeric and categorical data based on the density and distance

AU - Ji, Jinchao

AU - Pang, Wei

AU - Zheng, Yanlin

AU - Wang, Zhe

AU - Ma, Zhiqiang

PY - 2015/11

Y1 - 2015/11

N2 - Most of the initialization approaches are dedicated to the partitional clustering algorithms which process categorical or numerical data only. However, in real-world applications, data objects with both numeric and categorical features are ubiquitous. The coexistence of both categorical and numerical attributes make the initialization methods designed for single-type data inapplicable to mixed-type data. Furthermore, to the best of our knowledge, in the existing partitional clustering algorithms designed for mixed-type data, the initial cluster centers are determined randomly. In this paper, we propose a novel initialization method for mixed data clustering. In the proposed method, both the distance and density are exploited together to determine initial cluster centers. The performance of the proposed method is demonstrated by a series of experiments on three real-world datasets in comparison with that of traditional initialization methods.

AB - Most of the initialization approaches are dedicated to the partitional clustering algorithms which process categorical or numerical data only. However, in real-world applications, data objects with both numeric and categorical features are ubiquitous. The coexistence of both categorical and numerical attributes make the initialization methods designed for single-type data inapplicable to mixed-type data. Furthermore, to the best of our knowledge, in the existing partitional clustering algorithms designed for mixed-type data, the initial cluster centers are determined randomly. In this paper, we propose a novel initialization method for mixed data clustering. In the proposed method, both the distance and density are exploited together to determine initial cluster centers. The performance of the proposed method is demonstrated by a series of experiments on three real-world datasets in comparison with that of traditional initialization methods.

KW - clustering

KW - data mining

KW - mixed numeric and categorical data

KW - cluster center initialization

U2 - 10.1142/S021800141550024X

DO - 10.1142/S021800141550024X

M3 - Article

VL - 29

JO - International Journal of Pattern Recognition and Artificial Intelligence

JF - International Journal of Pattern Recognition and Artificial Intelligence

SN - 0218-0014

IS - 7

M1 - 1550024

ER -