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A New Approach Of Data Clustering Using a Flock Of Agents

A New Approach Of Data Clustering Using a Flock Of Agents. By A.Tarun Kumar. Contents. Introduction to Clustering Biological Model Main Algorithm Local Behavioral rule Experimental Results Improved algorithm

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A New Approach Of Data Clustering Using a Flock Of Agents

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  1. A New Approach Of Data Clustering Using a Flock Of Agents By A.Tarun Kumar

  2. Contents • Introduction to Clustering • Biological Model • Main Algorithm • Local Behavioral rule • Experimental Results • Improved algorithm • Real world application • conclusion

  3. Introduction To Clustering Clustering is one of the well-known techniques with successful applications on large domain for finding patterns. Clustering is useful in several exploratory pattern-analysis, grouping, decision making, and learning situations, including data mining, document retrieval, image segmentation, and pattern classification. Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters).

  4. Components of Clustering Task Feature Selection: Identifying the most effective subset of the original features to use in clustering Feature Extraction: Transformations of the input features to produce new salient features Inter pattern Similarity: measured by a distance function defined on pairs of patterns. Grouping: methods to group similar patterns in the same cluster

  5. Biological Model An illustration of the environment in which the flock of agents moves. Starting from the initial situation (a) where agents are randomly placed in the environment (with random direction), one wishes to obtain a final situation (b) where similar agents move in a coherent way (in the same direction and at a short distance from each others). a) b)

  6. Main Algorithm

  7. Local Behavioral Rule

  8. Change Of Direction: If sim(i,j) is equal to threshold then Where sim average and sim max are the mean and maximum similarity between the n data Where Vij is a unit vector pointing from i to j, beta (i,j) takes positive zero or Negative values.

  9. Experimental Results

  10. Improved Algorithms 1.Complexity reduction a. computing the neighborhood with a matrix. b. initialization with background knowledge 2. Interaction with the user a. Interactive clustering b. Generalization to the 3D with the stereoscopic vision

  11. An example of how background knowledge can be used to initialize the lo- cation/direction of the agents: in (a) the Iris database with random initialization, in (b) the same database with oriented initialization, in (c) and (d) the results respectively obtained from situations (a) and (b) after 50 iterations (a) (b) (c) (d)

  12. Real World Application The improved Fclust algorithm has been applied to a real database and this has been done in Collaboration with research center on healthy human skin funded by channel.

  13. References • F. Picarougne, H. Azzag, G. Venturini, and C. Guinot. (2007). A New Approach of Data Clustering Using a Flock of Agents, Evolutionary Computation, vol. 15, no. 3, pp. 345-367. • Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys, 31(3), 264-323. • http://news.bbc.co.uk/earth/hi/earth_news/newsid_9175000/9175793.stm

  14. Thank You

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