1Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

TreeSOM: Cluster analysis in the self-organizing map

Advisor : Dr. Hsu

Presenter : Zih-Hui Lin

Author :Elena V. Samsonovaa, Joost N. Kokb, Ad P. IJzermana

Neural Networks 19 (2006) 935–949

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Motivation Objective Introduction Method Conclusions Case studies

Outline

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Motivation

different map initializations and input order of data elements may result in different clusterings─ This is a lengthy and laborious task, so far not automat

ed. ─ For large data sets it becomes intractable for manual an

alysis, forcing the user to select a single “good” SOM and accept it as the final result omitting tests of confidence.

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Objective

We present a new method for cluster analysis and finding reliable clustering.

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Method 100 SOMs with different random seeds were produced (

Protein data)─ Map size 5 x 4─ Phase 1: starting learning rate 0.2, starting radius 6, 1000 iterations; ─ phase 2: starting learning rate 0.02, starting radius 3, 100,000 iterations.

Clustering ─ Cluster discovery─ Calibration

Consensus trees ─ SOM as a Tree─ Clustering confidence─ The most representative SOM

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Clustering 2.1. Cluster discovery 2.2. Calibration

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I. M.Consensus tree

3.1. SOM as a tree

The Besting clustering

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1.all the nodes in all the trees in the set are evaluated in terms of their leaf sets, and equivalent nodes are grouped.2.The number of nodes in each equivalence group divided by the total number of nodes in all trees gives the confidence value of each node in the group.3.

4. The distance between two sets P and Q equals the average distance between each element from P and from Q.

Consensus tree

3.2 Clustering confidence

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Consensus tree 3.3. The most representative SOM

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I. M.Conclusions

In this paper we present a new look at self-organizing maps, improving their applicability to clustering problems and facilitating comparisons of clustering results with those of hierarchical classifiers.

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Introduction

Protein data─ GPCRs

Consensus tree vs. phylogenetic trees

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I. M.Case study

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I. M.My opinion

Advantage We can find the most confident clusters easily.

Drawback ….

Application …..