Categorical Data Clustering Using Harmony Search Algorithm for Healthcare Datasets

被引：3

作者：

Sharma, Abha ^{[1
]}

Kumar, Pushpendra ^{[2
]}

Babulal, Kanojia Sindhuben ^{[2
]}

Obaid, Ahmed J. ^{[3
]}

Patel, Harshita ^{[4
]}

机构：

[1] Chandigarh Univ, Univ Inst Comp, Chandigarh, India

[2] Cent Univ Jharkhand, Dept Comp Sci & Technol, Ranchi, India

[3] Univ Kufa, Fac Comp Sci & Math, Dept Comp Sci, Kufa, Iraq

[4] Vellore Inst Technol, Sch Informat Technol & Engn, Vellore, Tamil Nadu, India

来源：

INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS | 2022年 / 13卷 / 04期

关键词：

Categorical data; Clustering; Genetic algorithm; Harmony search; Healthcare dataset; Premature; OPTIMIZATION;

D O I：

10.4018/IJEHMC.309440

中图分类号：

R-058 [];

学科分类号：

摘要：

Healthcare analytics provide many benefits in healthcare dashboard systems. Healthcare datasets majorly contains categorical attributes. This paper proposed an optimized clustering for healthcare dataset named harmony search based categorical clustering (HSCC). The existing k-modes clustering algorithm is one of the well-known categorical data-clustering algorithm. Since the k-modes algorithm produces local optimal clusters. Generally, researchers use genetic algorithm (GA) based clustering algorithms to converge locally optimal solutions to global optimal solutions. GA has some deficiencies such as premature convergence with low speed. In this paper, harmony search (HS) optimization algorithm used to optimize clustering results. The result shows the proposed HSCC algorithm produced global optimized solution, unbiased and matured results. HSCC produces 98% accuracy for dental and 71% for lung cancer dataset. While GACC produces 95% and 65% accuracy for dental dataset and lung cancer dataset.

引用

页数：15