Improvement on Agglomerative Hierarchical Clustering Algorithm Based on Tree Data Structure with Bidirectional Approach

Hierarchical clustering algorithms take an input of pair wise data-item similarities and output a hierarchy of the data-items. This paper presents bi-directional agglomerative hierarchical clustering algorithm to create a bottom-up hierarchy, by iteratively merging the closest pair of data-items int...

Full description

Saved in:
Bibliographic Details
Published in:2012 Third International Conference on Intelligent Systems Modelling and Simulation pp. 25 - 30
Main Authors: Dalbouh, Hussain Abu, Norwawi, Norita Md
Format: Conference Proceeding
Language:English
Published: IEEE 01.02.2012
Subjects:
Algorithm design and analysis
Bidirectional algorithm
Clustering algorithms
Complexity
Complexity theory
Compounds
Hierarchical
Partitioning algorithms
Tin
Tree
Tree data structures
ISBN:1467308862, 9781467308861
ISSN:2166-0662
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Hierarchical clustering algorithms take an input of pair wise data-item similarities and output a hierarchy of the data-items. This paper presents bi-directional agglomerative hierarchical clustering algorithm to create a bottom-up hierarchy, by iteratively merging the closest pair of data-items into one cluster. The result is a rooted AVL tree. The n leafs correspond to input data-items that need to n/2 or n/2+1 steps to merge into one cluster, correspond to groupings of items in coarser granularities climbing towards the root. As observed from the time complexity and number of steps needed to cluster all data points into one cluster perspective, the performance of the bi-directional agglomerative algorithm using tree data structure is better than the current agglomerative algorithms. Analysis on the experimental results indicates that the improved algorithm has a higher efficiency than previous methods.
ISBN:1467308862
9781467308861
ISSN:2166-0662
DOI:10.1109/ISMS.2012.13