1. 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

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 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

4. 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)

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 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 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.
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

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. 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