a new initialization method for fuzzy c means using fuzzy subtractive clustering
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A new initialization method for Fuzzy C- Means using Fuzzy Subtractive Clustering. Thanh Le, Tom Altman University of Colorado Denver July 19, 2011. Overview. Introduction Data clustering: approaches and current challenges fzSC

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a new initialization method for fuzzy c means using fuzzy subtractive clustering

A new initializationmethod for Fuzzy C-MeansusingFuzzySubtractiveClustering

Thanh Le, Tom Altman

University of Colorado Denver

July 19, 2011

overview
Overview
  • Introduction
    • Data clustering: approaches and current challenges
  • fzSC
    • a novel fuzzy subtractive clustering method for FCM parameter initialization
  • Datasets
    • artificial and real datasets for testing fzSC
  • Experimental results
  • Discussion
clustering problem
Clustering problem
  • Data points are clustered based on
    • Similarity
    • Dissimilarity
  • Clusters are defined by
    • Number of clusters
    • Cluster boundaries & overlaps
    • Compactness within clusters
    • Separation between clusters
clustering approaches
Clustering approaches
  • Hierarchical approach
  • Partitioning approach
    • Hard clustering approach
      • Crisp cluster boundaries
      • Crisp cluster membership
    • Soft/Fuzzy clustering approach
      • Soft/Fuzzy membership
      • Overlapping cluster boundaries
      • Most appropriate for the real problems
fuzzy c means algorithm
Fuzzy C-Means algorithm
  • The model
  • Features:
    • Fuzzy membership, soft cluster boundaries
    • Each data point can belong to multiple clusters, more relationship information provided
fuzzy c means contd
Fuzzy C-Means (contd.)
  • Possibility-based model
  • Fuzzy sets to describe clusters
  • Model parameters estimated using an iteration process
  • Rapid convergence
  • Challenges:
    • Determining the number of clusters
    • Initializing the partition matrix to avoid local optima
methods for partition matrix initialization
Methods for partition matrix initialization
  • Based on randomization
    • Problem:
      • Different randomization methods depend on different data distributions
  • Using heuristic algorithms: Particle Swarm
    • Problem:
      • Slow convergence because of velocity adjustment
  • Integrated with optimization algorithms
    • Problem:
      • Still based on other methods of partition matrix initialization
methods for partition matrix contd using subtractive clustering
Methods for partition matrix…(contd) using Subtractive Clustering
  • Mountain function; the data density,

, : mountain peak radius

  • Mountain amendment; density adjustment,

, : mountain radius

  • Cluster candidate; the most dense data point

, : threshold to stop the cluster center selection

subtractive clustering method the problems
Subtractive Clustering methodThe problems

NO

  • Mountain peak radius? 

OK

NO

  • Mountain radius? 

OK

  • Remaining density to be selected? 
  • Computational time: O(n2)
the proposed method fzsc for partition matrix initialization
The proposed method: fzSCfor partition matrix initialization
  • Generate a random fuzzy partition
  • Compute cluster density using histogram
  • Use strong uniform fuzzy partition concept
  • Estimate mountain function based on cluster density
  • Amend mountain function:
    • Update cluster density (step 2)
    • Re-estimate mountain function (step 4)
fzsc optimal number of clusters
fzSC:Optimal number of clusters
  • The most dense data point is a cluster candidate
    • Data density is not much affected, say less than 0.05 of the data density removed by the mountain function amendment process.
    • The number of such points is less than n
  • , ,  are not required
  • Computational time: O(c*n)
datasets
Datasets
  • Artificial datasets
    • Finite mixture model based datasets
    • A manually created (MC) dataset
      • Data were generated using finite mixture model
      • Clusters were moved to have different distances among clusters
  • Real datasets

Iris, Wine, Glass and Breast Cancer Wisconsin datasets at UC Irvine Machine Learning Repository

visualization of fzsc result on the manually created mc dataset
Visualization of fzSC result on the manually created (MC) dataset

Rectangles- cluster centers of random fuzzy partition, Circles- cluster centers by fzSC

a visualization
A visualization…

Stars- cluster centers of random fuzzy partition, Circles- cluster centers by fzSC

The utility is available online: http://ouray.ucdenver.edu/~tnle/fzsc/

experimental results on manually created dataset
Experimental results onmanually created dataset

The algorithm performance on the MC dataset

experimental results on artificial datasets
Experimental results onartificial datasets

Correctness ratio in determining cluster number

experimental results on real datasets
Experimental results onReal datasets

Correctness ratio in determining cluster number

discussion the advantages of fzsc
Discussion:The advantages of fzSC
  • Traditional subtractive clustering
    • , ,  are not required
    • Computational time O(c*n) vs. O(n2)
  • Heuristic based approaches
    • Rapid convergence
    • Escape local optima
  • Probability model based
    • Rapid convergence
    • No assumption of data distribution
discussion future work
Discussion:Future work
  • Combine fzSC with biological cluster validation methods and optimization algorithms for novel clustering algorithms regarding the gene expression data analysis problem.
thank you
Thank you!

Questions?

  • We acknowledge the support from
    • Vietnamese Ministry of Education and Training, the 322 scholarship program.
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