A Hybrid Clustering Approach for Diagnosing Medical Diseases

被引：10

作者：

Simic, Svetlana ^{[1
]}

Bankovic, Zorana ^{[2
]}

Simic, Dragan ^{[3
]}

Simic, Svetislav D. ^{[3
]}

机构：

[1] Univ Novi Sad, Fac Med, Hajduk Veljkova 1-9, Novi Sad 21000, Serbia

[2] Frontiers Media SA, Pozuelo Alarcon Sn, Madrid, Spain

[3] Univ Novi Sad, Fac Tech Sci, Trg Dositeja Obradovica 6, Novi Sad 21000, Serbia

来源：

HYBRID ARTIFICIAL INTELLIGENT SYSTEMS (HAIS 2018) | 2018年 / 10870卷

关键词：

Data clustering; Maximum likelihood estimates clustering; Number of clusters; Fuzzy partition method;

D O I：

10.1007/978-3-319-92639-1_62

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Clustering is one of the most fundamental and essential data analysis tasks with broad applications. It has been studied extensively in various research fields, including data mining, machine learning, pattern recognition, and in scientific, engineering, social, economic, and biomedical data analysis. This paper is focused on a new strategy based on a hybrid model for combining fuzzy partition method and maximum likelihood estimates clustering algorithm for diagnosing medical diseases. The proposed hybrid system is first tested on well-known Iris data set and then on three data sets for diagnosing medical diseases from UCI data repository.

引用

页码：741 / 752

页数：12

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