A Comparative Study of Clustering Algorithms for Mixed Datasets

被引：0

作者：

Harous, Saad ^{[1
]}

Al Harmoodi, Maryam ^{[1
]}

Biri, Hessa ^{[1
]}

机构：

[1] UAE Univ, Coll Informat Technol, Al Ain, U Arab Emirates

来源：

PROCEEDINGS 2019 AMITY INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AICAI) | 2019年

关键词：

Clustering; Mixed Data; K Means; Similarity measure; K-MEANS;

D O I：

10.1109/aicai.2019.8701347

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Clustering groups, a set of elements in a manner that elements in the same categoryhave more common characteristics (based on a given set of attributes) among themthan to elements in other categories. Each group is called a cluster. Clustering is used in many areas: sensor networks, social networks, health, business and other applications. There are many different clustering algorithms with different parameters. The appropriate clustering algorithm and parameter settings depend on data set and the problem being solved. Some work only on numerical data and other on mixed data. Our aim is to do a comparative study of these algorithms.

引用

页码：484 / 488

页数：5

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