Document Categorization and Query Generation on the World Wide Web Using WebACE

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Document Categorization and Query Generation on
the World Wide Web Using WebACE

• Daniel Boley etc.
• University of Minnesota
• Presented by
• Ya Sun Yanhong Zhang

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Contents

• Introduction
• Related Work
• WebACE Architecture
• Clustering Methods
• Experimental Evaluation
• Search for and Categorization of Similar
  Documents
• Conclusion

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Introduction

• Search engines often return inconsistent results
• WebACE an intelligent web agent
• Clustering two new partitioning-based algorithms
• Query generation
• Searching for similar web documents
• Filtering and classifying into existing clusters

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Related Work

• Intelligent Search Agents
• Interpret discovered information, e.g. FAQ-Finder
• Learn structures of unfamiliar sources, e.g. ILA
• Information Filtering/ Categorization
• Automatically retrieve, filter and categorize,
  e.g. HyPursuit
• Personalized Web Agents
• Learn user preference and discover related
  information, e.g. WebWatcher

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WebACE Architecture
Document vectors
clusters
queries
Profile Creation Module
Clustering Modules
Query Generator
Search Mechanism
User option
Filter (optional)
Clustering Updater(optional)
Document vectors
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Association Rule Hypergraph Partitioning Algorithm

• Hypergraph H (V, E)
• V a set of vertices, here, docs being clustered
• E a set of hyperedges which can connect more
  than two vertices, here, a set of related docs
• A weight is assigned to each hyperedge
• Transactional form of document retrieval
• Each document viewed as an item
• Each possible feature word as a transaction

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Association Rule Hypergraph Partitioning
Algorithm (Cont)

• Support Count
• T the set of transactions
• t transaction, a subset of the item-set I
• C a subset of I
• Association Rule
• X, Y subset of I
• Support s
• Confidence a
• Task find all rules with s and a greater than
  given minimums

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Association Rule Hypergraph Partitioning
Algorithm (Cont)

• The hypergraph representation
• Represent each document as a vertex item
• Compute all the frequent item-sets, with a given
  threshold support count
• Represent each frequent set as a hyperedge
• Assign the weight as the average confidence of
  the essential association rules of the set

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Association Rule Hypergraph Partitioning
Algorithm (Cont)

• The hypergraph partitioning
• Partition the hypergraph by minimizing the weight
  of the hyperedges that are cut
• Partition each part recursively
• Stop the partition with fitness criterion
• Filter out bad vertices by connectivity function

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Principal Direction Divisive partitioning

• Represent documents as a term frequency matrix M,
  
• Mij is the number of occurrences of word wi in
  document dj
• Results independent of document length
• Sparse, 3 of entries nonzero

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Principal Direction Divisive partitioning (Cont)

• The Centroid vector c for each cluster
• k the number of documents in the cluster
• The principal direction for each cluster
• The direction of maximum variance
• The principal eigenvector of the covariance
  matrix
• Obtained by computing the leading left singular
  vector of (M c e)
• Only matrix-vector products required, preserving
  the sparsity

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Principal Direction Divisive partitioning (Cont)

• Partitioning
• Project all documents onto the principal
  direction, classify them by the sign of the
  results
• Repeat the process on each cluster recursively
• Stop condition scatter of cluster
• Stops when scatter values of all individual
  clusters below that of the centroid vectors

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Experimental Evaluation

• Comparative Evaluation of Clustering Algorithms
• Compare ARHP, PDDP, HAC and AutoClass with LSI or
  without LSI
• 185 web pages in 10 categories BC, IP, EC, etc.
• The measure of goodness of the clusters Entropy
• Etotal ?j Ej nj /n
• Scalability of Clustering Algorithms
• PDDP
• ARHP

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10 Feature Selection Methods
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Comparative Evaluation of
Clustering Algorithms
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Comparative Evaluation of
Clustering algorithm
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Comparative Evaluation of
Clustering algorithm (Cont)
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Comparative Evaluation of
Clustering algorithm (Cont)
Figure 6 Entropy of different algorithms. Note
that lower entropy Indicates better cohesiveness
of clusters.
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Conclusions from experimental results

• Both PDDP and ARHP performed better than HAC and
  AutoClass regardless of feature selection
  criteria used
• Dramatic differences in run times of the four
  methods.
• ARHP, PDDP lt 2 mins
• HAC 1 hr. and 40 mins
• AutoClass 38 mins
• (dataset size of 185 x 10538)

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Dimensionality Reduction using LSI/SVD
Figure 7 Comparison of Entropies for the E1,
With and Without LSI.
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Scalability of Clustering Algorithms

• Data sets
• D1 2340 docs, 21,839 words
• D3, D9, D10 reduced dictionaries
• D3 8104 words
• D9 7358 words
• D10 1458 words

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Scalability of PDDP
Figure 8 Entropies form the PDDP algorithm with
various number of Clusters.
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Scalability of PDDP (Cont)
Figure 9 Times for the PDDP algorithm on an SCI
versus number of Nonzeros in term frequency
matrix, for both E and D series.
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Scalability of ARHP
Figure 10 Entropies from the ARHP algorithm with
various number of clusters.
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Scalability of ARHP (Cont)
Figure 11 Hypergraph partitioning time for both
E and D series
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Search for and Categorization of Similar
Documents

• A representative set of words used in a web
  search.
• TF the list of k words that have the highest
  average text frequency.
• DF the list of k words that have the highest
  document frequency.
• The query can be formed as
• (c1?c2 ? ? cm) ? (t1? t2 ? tn)
• Where ci ? TF ? DF , ti ? TF - DF

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Search for and Categorization of Similar
Documents ( Cont)

• The documents returned as the result of queries
  can be handled
• ARHP could be used to filter out non-relevant
  documents
• Added to the existing clusters using ARHP or PDDP

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Conclusion and Future Work

• Conclusion
• ARHP and PDDP are capable of extracting higher
  quality clusters.
• They are fast and scale with the number of words
  in the documents
• Future work
• Explore the performance of the entire agent as an
  integrated and fully automated system
• Development a method for evaluating quality of
  clusters which is not based on a priori class
  labels.