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Hanan Ayad Supervisor Prof. Mohamed Kamel

Developing Methods for Combining Multiple Clustering of Patterns Towards the discovery of natural clusters. Hanan Ayad Supervisor Prof. Mohamed Kamel. Outline. Motivations Research Summary 2003 Publications Application to Learning Objects Diverse Sources of Information

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Hanan Ayad Supervisor Prof. Mohamed Kamel

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  1. Developing Methods for Combining Multiple Clustering of PatternsTowards the discovery of natural clusters Hanan Ayad Supervisor Prof. Mohamed Kamel

  2. Outline • Motivations • Research Summary • 2003 Publications • Application to Learning Objects • Diverse Sources of Information • Multiple Clustering in LO • Process Overview

  3. Motivations • Multiple clustering solutions • Different clustering methods • Selection of learning parameters (e.g. NNets) • Random starts, random ordering of patterns • Enhance Quality • Compensatory effects in clustering methods • Repeated fine decompositions • Distributed clustering • Feature-Distributed • Multiple partial views • Random subspaces • Alternative feature reductions • Data-Distributed • Overlapping subsets of patterns

  4. Research Summary • Measure co-associations between patterns based on their co-clustering - voting • Development of combination rules based on shared co-associations – Shared nearestneighbors(binary votes, weighted votes, sum rule, product rule, rank-based rule) • Determine strength-of-association • Accumulate local neighborhood densities of patterns • Patterns weights are inversely proportional to their local neighbourhood densities(number and weights of relationships)

  5. Research Summary, Cont’d • Pruning of associations for efficiency and assessment of behaviour. • Effect on mutuality of relationships • Improving quality using subsets of patterns relations • Study of convergence and stability • Induce a graph, representing the patterns weighted relationships, and the patterns own weights. Weighted Shared nearest neighbors Graph (WSnnG) • The graph is partitioned resulting in an integrated clustering of the objects • Use of the graph partitioning package METIS. • Minimize edge-cut subject to weights of vertices being equally distributed among the clusters

  6. 2003 Publications • H. Ayad and M. Kamel. "Finding Natural Clusters Using Multi-Clusterer Combiner Based on Shared Nearest Neighbors“. Multiple Classifier Systems: Fourth International Workshop, MCS 2003, Guildford, Surrey, United Kingdom, June 11-13. Proceedings. • H. Ayad, and M. Kamel. Refined Shared Nearest Neighbors Graph for Combining Multiple Data Clusterings", The 5th International Symposium on Intelligent Data Analysis IDA 2003. Berlin, Germany. Proceedings. LNCS. Springer. August, 2003 • H. Ayad, and M. Kamel. Development of New Methods for Combining Cluster Ensembles. On going Journal Paper.

  7. Application to Learning Objects Diverse Sources of Information • Meta Data • Standardized Indexing, content structure and organization • Intelligent Content Mining • Natural Language Understanding • Image Analysis and Understanding • Automatic Speech Recognition • Statistical Learning • Re-Use/Learning Scenarios • Dynamic assembly, object are grouped and regrouped with other objects.

  8. Application to Learning Objects Multiple Clustering in LO • Clusters of Learning Objects • Multiple distributed taxonomies • Info. Sources: different sets of meta data, different re-use/assembling scenarios • Dynamic environment, clusters based on partial views • Combining of multiple clustering • Mining complex web of relationships • integrating multiple objects clustering • Discovery of combined multi-view clusters.

  9. Meta Data 1 . . . Meta Data m Re-use Scenarios 1 . . . Re-use Scenarios s Application to Learning Objects Process Overview Clustering 1 . . . Combining Multiple Objects Clustering Integrated Learning Objects Clusters Learning Objects Clustering r

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