Hierarchical Sequence Clustering Algorithm for Data Mining

被引：0

作者：

Chezhian, V. Umadevi ^{[1
]}

Subash, Thanappan ^{[2
]}

Samy, M. Ragavan ^{[3
]}

机构：

[1] Coll Business & Econ, Dept Comp Sci, Asmera, Eritrea

[2] Eritrea Inst Technol, Dept Civil Engn, Abardae, Eritrea

[3] Singapore Refinery Co Pvt Ltd, Singapore, Singapore

来源：

WORLD CONGRESS ON ENGINEERING, WCE 2011, VOL III | 2011年

关键词：

Data Mining; Hierarchical Clustering; Sequence Clustering; Probabilistic Suffix Tree; UPMGA; SUFFIX TREE CONSTRUCTION;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Bioinformatics emerged as a challenging new area of research and brought forth numerous computational problems. Here computers are used to gather, store, analyze and merge biological data. In this paper, the problem of clustering interval-scaled data and sequence data is analyzed in a new approach using Hierarchical Sequence Clustering. In Sequence clustering, it is necessary to find the similarity or distance between each pair of sequences. To find the similarity between sequences the data structure Probabilistic Suffix Tree can be used. An agglomerative algorithm is introduced based on UPGMA (Un weighted Pair wise Group Average Method) cluster analysis, that required O(n(3)) of total computing time: Then a new algorithm using the new approach is introduced with O(n(2)) computing time. The result of this new algorithm is compared with UPGMA cluster analysis.

引用

页码：1861 / 1864

页数：4

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