Evolutionary hierarchical time series clustering

被引：0

作者：

Chis, Monica ^{[1
]}

Grosan, Crina ^{[2
]}

机构：

[1] Avram Iancu Univ, Cluj Napoca, Romania

[2] Univ Babes Bolyai, R-3400 Cluj Napoca, Romania

来源：

ISDA 2006: SIXTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, VOL 1 | 2006年

关键词：

time series; hierarchical clustering; evolutionary computation; evolutionary hierarchical clustering;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Time series clustering is an important topic, particularly for similarity search amongst long time series such as those arising in bioinformatics. In this paper a new evolutionary algorithm for detecting the hierarchical structure of an input time series data set is proposed. A new linear representation of the cluster structure within the data set is used. Proposed algorithm uses mutation and crossover as (search) variation operators. A new fitness function is proposed.

引用

页码：451 / 455

页数：5

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