Xavier Polanco

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Textual Information Clusteringand Visualization
for Knowledge Discovery and Management

• Xavier Polanco
• URI-INIST-CNRS

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Introduction

• We are concerned with the design and development
  of computer-based information analysis tools
• Cluster analysis, computational linguistics and
  artificial intelligence techniques are combined

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On the technology side

• An information analysis computer-based system is
• an integrated environment that somehow assisted a
  user
• in carrying out the complex process of converting
  information from the textual data sources to
  knowledge

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Information Analysis System
Lexicons or terminological resources
French or English text-data
Dataset or Corpus
Clustering and Mapping
DBMS-R
Term Extraction And Indexation
Bibliometric statistics
WWW Server
SDOC
HENOCH
NEURODOC
MIRIAD
ILC
Mac
PC
WS
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Home Pages
Intranet
Extranet
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Plan

• Text Mining
• Cluster Analysis
• Visualization or Mapping
• Knowledge Discovery
• Knowledge Management

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Textual Information

• Big amount of information is available in textual
  form in databases and online sources
• In this context, manual analysis and effective
  extraction of useful information are not possible
  
• It is relevant to provide automatic tools for
  analyzing large textual collections

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Text Mining

• Text mining consists of extraction information
  from hidden patterns in large text-data
  collections
• The results can be important both
• for the analysis of the collection, and
• for providing intelligent navigation and browsing
  methods

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Process

• The text mining process can be organized roughly
  into five-major steps
• Data Selection
• Term Extraction and Filtering
• Data Clustering and Classification
• Mapping or Visualization
• Result Interpretation
• Iterative and interactive process

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Natural Language Processing

• Experience shows that linguistic engineering
  approach insures a higher performance of the data
  mining algorithms
• Part-of-speech tagging (tagging texts), and
  lemmatization are tasks generally admit

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The approach

• Our approach to text mining is based on
  extracting meaningful terms from documents
• In this presentation, the focus is on the term
  extraction process, and
• The need of the organization of the generated
  terms in a taxonomy

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The main tasks

• Term extraction or acquisition
• Indexation
• Human control and screening
• Indexing quality control
• Index screening ? clustering phase

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Language Engineering
Natural Language Engineering System
Lexicons
Text-DB
Indexed Corpus
Lexicons Management and Linguistic
Processing Texts Part-of-speech tagging,
lemmatization, and indexation
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Variation
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Taxonomy

• A taxonomic structure should improve text mining
• Considering the clustering techniques that might
  be used in text mining. One must be mindful that
  more taxonomic classifying capabilities would be
  incorporated into text mining
• A taxonomic classifying capability might also
  facilitate cluster interpretation by giving the
  user some kind of rules

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Clustering

• Clustering is a descriptive task where one seeks
  to identify a finite set of categories
• Clustering is used to segment a database into
  subsets or clusters
• Clustering means finding the clusters themselves
  from a given set of data

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Clustering Process
Similarity Measures s(x,y)
Clustering Algorithm
D(n,p)
C(m,p)
Dissimilarity Measures d(x,y)
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Documents ? Keywords
KW1 KW2 KW3 KW4 KW5 KW6
D1 1 0 1 0 1
1 D2 1 0 1 0 1
1 D3 0 1 0 1 0
0 D4 1 0 0 1 0
1
Di ? KWj 1,0 Di ? KWj 1, 2, , n
C1 (D1,D2KW1,KW3,KW5,KW6) C2
(D4KW1,KW4,KW6) C3 (D3KW2,KW4)
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Clustering Algorithms

• Major families of clustering methods
• Sequential algorithms
• Hierarchical algorithms
• Agglomerative algorithms
• Divisive algorithms
• Fuzzy clustering algorithms

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Information Analysis Process

• The text-data information analysis is divided
  into two phases
• Cluster generation
• Map display of clusters
• A hypertext user interface enables the analyst to
  explore and interpret results

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Example
Antibiotic Resistance
2 DB
4025 documents (1998-1999)
Data
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Medicine
Molecular Biology
Hypertext
Clusters
Map
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Information Visualization

• Definition The use of computer-supported,
  interactive, visual representation of abstract
  data to amplify the acquisition or use of
  knowledge (Card et al., 1999)
• Visual artifacts aid human thought
• The progress of civilization can be read in the
  invention of visual artifacts, from writing to
  mathematics, to maps, to diagrams, to visual
  computing

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Process

• Raw Data ? Data Tables
• Data Tables ? Clustering
• Clustering ? Visual Structures Map
• Visual Structures ? Views

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Visual Structures

• Data Tables are mapped to Visual Structures,
  which augment a spatial substrate with marks and
  graphical properties to encode information
• A Graphic Representation is said to be expressive
  if all and only the data in the Data Table are
  also represented in the Visual Structure
• A Graphic Representation is said to be more
  effective if it is faster to interpret

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Map Display

• We are concerned with map display of the clusters
• A problem of particular interest is how to
  visualize data set with many variables
• Multivariate-Data are clustered, and
• Clusters are mapped

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Mapping tools

• For mapping, we use the following techniques
• Density and Centrality Diagrams
• Principal Component Analysis (PCA)
• Multi-Layer Perceptrons (MLP)
• Self-Organizing Maps (SOM)
• Multi-SOMs

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Multi-Layer Perceptron 1
ISEs-x2
prion
proteins
Wcij
Wsjk
s1
scrapie
x1
sk
xi
human disease
spongiform encephalopathy
mankind
Wc(p,2)
Ws(2,p)
xp
sp
CJD
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Multi-Layer Perceptron 2
protein
infection resistance
Agrobacterium
plasmids
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Multi-SOM Platform
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Multi-Self-Organizing Map Display
Maps associated to 5 viewpoints Map 1 ?
Plants Map 2 ? Plant Parts Map 3 ? Pathogen
Agents Map 4 ? Genetic Techniques Map 5 ?
Patenting Firms
5
4
2
1
Rice Area Activated
Use of the inter-Map Communication Mechanism
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Knowledge Discovery

• KD is informally defined as the extraction of
  useful knowledge from databases or large amounts
  of data
• One of the most important research topics in KD
  is the rule discovery or extraction
• The discovered knowledge is usually expressed in
  the form of  if-then  rules

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Association Rules

• Association rules can be seen as one of the key
  tasks of KDD
• The intuitive meaning of an association rule X ?
  Y, where X and Y are keywords or descriptors, is
  a document set containing keyword X is likely
  to also contain keyword Y

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Example

• In a given a food-industry corpus
• 98 of the documents which are interested on
  apple juice does it related with the
  chromatography analytic technique
• X ? Y apple juice ? chromatography

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The Galois Lattice

• Our current research includes an approach based
  on the lattice structure to discover concepts and
  rules to the objects (documents) and their
  properties (keywords)
• The Galois lattice approach is also known as
  conceptual clustering

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The concept lattice
Given the context (D1,T1) where D1
d1,d2,d3,d4 T1 t1,t2,t3,t4,t5,t6
Hasse Diagram
C1(D1,Ø)
R t1 t2 t3 t4 t5 t6 d1 1 0 1 0 1 1 d2 1
0 1 0 1 1 d3 0 1 0 1 0 0 d4 1 0 0 1
0 1
C2(d1,d2,d4,t1,t6
C3(d3,d4,t4
C4(d1,d2,t1,t3,t5,t6
C5(d4,t1,t4,t6
C6(d3,t2,t4
Table The input relation R documents ?
keywords
C7(Ø, T1)
The formal concept C4 has two own terms t3,t5
and two inherited terms t1,t6
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Association Rules Extraction

• The formal concept C4 makes it possible the
  following rules
• R1 t3 ? t1 ? t6
• R2 t5 ? t1 ? t6
• R3 t3 ? t5
• The interpretation of the R1 and R2 The use of
  terms t3 or t5 is always associated with that of
  terms t1 and t6
• The rule R3 express mutual equivalence of the
  terms t3,t5 All the documents which have the
  term t3 also have the t5 term.

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Summary
Text Mining
Clustering
Mapping
Knowledge Discovery
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Knowledge Management

• A knowledge management system is concerned with
  the identification, acquisition, development,
  diffusion, use, and preservation of the
  enterprises knowledge

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KM Objectives

• Using advanced technology
• For facilitating creation, access, and reuse of
  knowledge
• For converting knowledge from the sources
  accessible to an organization and connecting
  people with that knowledge

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Project

• Adding to the information analysis system a
  formalized operator for processing together
• The knowledge that is extracted from databases
• The knowledge that the experts produce when they
  analyze the clusters, maps, concepts and rules

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We have reached our last subject, but not the
end !
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Merci
Gracias
Obrigado
Thanks
Xavier Polanco