Dynamic Hybrid Clustering of Bioinformatics by Incorporating Text Mining and Citation Analysis

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Dynamic Hybrid Clustering of Bioinformatics by
Incorporating Text Mining and Citation Analysis

• Frizo Janssens, Wolfgang Glänzel, and Bart De
  Moor

Presented by Cindy Burklow
CS 685 Special Topics in Data Mining Professor
Dr. Jinze Liu University of Kentucky April 17th,
2008
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Outline

• Introduction
• Motivation
• Related Work
• Proposed Models
• Proposed Algorithms
• Results Hybrid Dynamic Clustering
• Discussion of Pros and Cons
• Questions
• References

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Introduction

• Bioinformatics
• Computer Science
• Information Technology
• Solves problems in Biomedicine
• Goal of Paper Investigate
• Cognitive structure
• Dynamics of bioinformatics core
• Sub-disciplines
• ISI Web of Science MEDLINE
• Retrieval of core literature in bioinformatics

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MeSH Medical Subject Headings
Image Reference Wolfgang Glnzel, and Bart De
Moor, Dynamic hybrid clustering of
bioinformatics by incorporating text mining and
citation analysis, pg. 360, 368, KDD '07. ACM,
San Jose, CA, August 2007.
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Motivation

• Bioinformatics field
• Dynamic
• Evolving discipline
• Fast growth rate
• Monitor current trends
• Predict future direction
• Decision Making
• Grants
• Business Ventures
• Research Opportunities

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Image Reference Wolfgang Glnzel, and Bart De
Moor, Dynamic hybrid clustering of
bioinformatics by incorporating text mining and
citation analysis, pg. 361, 368, KDD '07. ACM,
San Jose, CA, August 2007.
9
Related Work

• Web mining
• Bibliometrics
• Text mining citation analysis
• Mapping of knowledge
• Charting science technology fields
• Textual graph-based approaches
• Different perceptions of similarity between
  documents or groups of documents

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Related Work

• Establishing the Data Set
• Patra Mishra Bibliometric Study
• MeSH term based
• Liberal delineation strategy with maximal recall
• Broader interpretation of bioinformatics
• Less restricted search strategy
• Broader coverage of underlying database
• 14,563 journal papers

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Related Work

• Hybrid Clustering
• He Unsupervised spectral clustering of web
  pages
• Wang Kitsuregawa Contents-linked coupled
  clustering algorithm of web pages
• Dynamic hybrid clustering
• Mei Zhai Temporal Text Mining
• Kullback-Leibler Divergence for coherent themes
  Hidden Markov Models
• Griffiths Steyvers Latent Dirichlet
  Allocation with hot topics in PNAS abstracts

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Models Data SetBibliometric Retrieval Strategy

• Novel subject delineation strategy
• Retrieve core literature
• Combines textual components bibliometrics,
  citation-based techniques
• Web of Science Edition of Thomson Scientific
• 7401 bioinformatics-related papers
• 1981 to 2004
• Titles, abstracts, author keywords, and MeSH
  terms

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Models Text Analysis

• All text was indexed with Jakarta Lucene Platform
• Encoded in Vector Space Model using TF-IDF
  weighting scheme
• Text-based similarities
• Cosine of angle between the vector
  representations of two papers
• No Stop word used during indexing
• Porter Stemmer
• All remaining terms from titles and abstracts
• Bigrams
• Candidate list of MeSH descriptors, author
  keywords, and noun phrases
• Latent Semantic Indexing (LSI) 10 terms

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Models Citation Analysis

• Citation Graphs
• Link-based algorithms
• HITS
• PageRank

Representative Publications
Combine
Image Reference Google Logo from
http//www.google.com
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Models Clustering

• Agglomerative Hierarchical Clustering Algorithm
  with Wards Method
• Hard Clustering Algorithm
• Every publication is assigned to exactly 1
  cluster.

Image Reference Clustering Analysis -
http//en.wikipedia.org/wiki/Data_clustering
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Models Clustering

• Optimal number of clusters
• Combine Distance-based Stability-based Methods
  Strategy

Silhouette Curves Mean text and Citation-based
Dendrogram observation
Stability Diagram
Image Reference Wolfgang Glnzel, and Bart De
Moor, Dynamic hybrid clustering of
bioinformatics by incorporating text mining and
citation analysis, pg. 364, 365, KDD '07. ACM,
San Jose, CA, August 2007.
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Proposed Algorithm Hybrid Clustering

• Cluster Input Distances
• Combining text mining and bibliometrics
• Integrate text citation info early in mapping
  process before applying of clustering algorithm
• Weighted linear combination
• Fishers inverse chi-square method

Image Reference Wolfgang Glnzel, and Bart De
Moor, Dynamic hybrid clustering of
bioinformatics by incorporating text mining and
citation analysis, pg. 362, 363, KDD '07. ACM,
San Jose, CA, August 2007.
18
Image Reference Wolfgang Glnzel, and Bart De
Moor, Dynamic hybrid clustering of
bioinformatics by incorporating text mining and
citation analysis, pg. 363 KDD '07. ACM, San
Jose, CA, August 2007.
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Proposed Algorithm Dynamic Hybrid Clustering

• Goal Match track clusters through time
• Process
• Separate hybrid clustering for each period
• Determine optimal number of clusters
• Dendrogram
• Silhouette curve
• Ben-hur stability plot
• Construct complete graph
• All cluster centroids from each period as nodes
• Edge weights as mutual cosine similarities in LSS
• Form Cluster Chains
• Keep edge weights gt threshold, T1
• Allow qualifying clusters to join gt threshold, T2

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Image Reference Wolfgang Glnzel, and Bart De
Moor, Dynamic hybrid clustering of
bioinformatics by incorporating text mining and
citation analysis, pg. 367, KDD '07. ACM, San
Jose, CA, August 2007.
21
Image Reference Wolfgang Glnzel, and Bart De
Moor, Dynamic hybrid clustering of
bioinformatics by incorporating text mining and
citation analysis, pg. 367, KDD '07. ACM, San
Jose, CA, August 2007.
22
Results Hybrid ClusteringSilhouette Curve
Image Reference Wolfgang Glnzel, and Bart De
Moor, Dynamic hybrid clustering of
bioinformatics by incorporating text mining and
citation analysis, pg. 364, KDD '07. ACM, San
Jose, CA, August 2007.
23
Result Hybrid ClusteringSilhouette Curve
Image Reference Wolfgang Glnzel, and Bart De
Moor, Dynamic hybrid clustering of
bioinformatics by incorporating text mining and
citation analysis, pg. 364, KDD '07. ACM, San
Jose, CA, August 2007.
24
Result Hybrid ClusteringStability
Image Reference Wolfgang Glnzel, and Bart De
Moor, Dynamic hybrid clustering of
bioinformatics by incorporating text mining and
citation analysis, pg. 365, KDD '07. ACM, San
Jose, CA, August 2007.
25
Result Hybrid ClusteringDendrogram
Image Reference Wolfgang Glnzel, and Bart De
Moor, Dynamic hybrid clustering of
bioinformatics by incorporating text mining and
citation analysis, pg. 365, KDD '07. ACM, San
Jose, CA, August 2007.
26
Result Hybrid ClusteringCluster
Characterization
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Result Dynamics ClusteringHistogram
Image Reference Wolfgang Glnzel, and Bart De
Moor, Dynamic hybrid clustering of
bioinformatics by incorporating text mining and
citation analysis, pg. 365, KDD '07. ACM, San
Jose, CA, August 2007.
28
Result Dynamics ClusteringCluster Chains
Image Reference Wolfgang Glnzel, and Bart De
Moor, Dynamic hybrid clustering of
bioinformatics by incorporating text mining and
citation analysis, pg. 367, KDD '07. ACM, San
Jose, CA, August 2007.
29
Yearly Publication Output among Cluster chains
Image Reference Wolfgang Glnzel, and Bart De
Moor, Dynamic hybrid clustering of
bioinformatics by incorporating text mining and
citation analysis, pg. 368, KDD '07. ACM, San
Jose, CA, August 2007.
30
Dynamic Term Network
Image Reference Wolfgang Glnzel, and Bart De
Moor, Dynamic hybrid clustering of
bioinformatics by incorporating text mining and
citation analysis, pg. 368, KDD '07. ACM, San
Jose, CA, August 2007.
31
Pros Cons

• Pros
• Offers fresh perspective on clustering
• Integrates various techniques
• Provides insight into bioinformatics
• Cons
• Challenge of selecting the optimal number of
  clusters still exists
• There are many steps required to implement their
  approach

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Questions
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References

• Janssens, F., Glänzel, W., and De Moor, B.
  2007. Dynamic hybrid clustering of
  bioinformatics by incorporating text mining and
  citation analysis. In Proceedings of the 13th ACM
  SIGKDD international Conference on Knowledge
  Discovery and Data Mining (San Jose, California,
  USA, August 12 - 15, 2007). KDD '07. ACM, New
  York, NY, 360-369. DOI http//doi.acm.org/10.1145
  /1281192.1281233
• ISI Web of Science Image http//apps.isiknowledge
  .com/WOS_GeneralSearch_input.do?highlighted_tabWO
  SproductWOSlast_prodWOSSID3DamC8GFDKmpBLhFOI
  Msearch_modeGeneralSearch
• PubMed Image http//www.ncbi.nlm.nih.gov/pubmed/
  
• The Apache Jakarta Project http//lucene.apache.
  org/java/1_4_3/
• Fishers Method http//en.wikipedia.org/wiki/Fis
  her27s_method
• Data Mining - Concepts and techniques by Han
  and Kamber, Morgan Kaufmann, 2006.
  (ISBN1-55860-901-6)