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 Multiple Clustering in LO Process Overview

Motivations Multiple clustering solutions Enhance Quality 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

Research Summary Measure co-associations between patterns based on their co-clustering - voting Development of combination rules based on shared co-associations – Shared nearest neighbors (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)

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

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.

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. Are any “re-usable”, self-contained pieces of educational material, including text, audio clips, images, videos, animations, which can be dynamically assembled and used in technology supported learning. Are organized in Repositories. A learning object repository organizes educational content using a standardized indexing system. Items contained within a repository can include text, audio clips, images, videos, animations, and more complex combinations of digital elements. The Learning Object Metadata standards focus on the minimal set of attributes needed to allow these Learning Objects to be managed, located, and evaluated. The standards will accommodate the ability for locally extending the basic fields and entity types. Intelligent Content Mining generate new Meta Data for the Learning Objects. It is possible for any given Learning Object to have more than one set of Learning Object Metadata Different re-use or learning scenarios are rich and diversified sources of information, can be aggregated and used for search and categorization

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.

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