Cooperative clustering
Cooperative Clustering or Cooperative-Based Clustering is a model that involves multiple clustering techniques; the goal of the cooperative model is to increase the homogeneity of objects within clusters through cooperation by developing two data structures, cooperative contingency graph and histogram representation of pair-wise similarities.[1]
Description[edit]
Analysis of data can reveal interesting, and sometimes important, structures or trends in the data that reflect a natural phenomenon. Discovering regularities in data can be used to gain insight, interpret certain phenomena, and ultimately make appropriate decisions in various situations. Finding such inherent but invisible regularities in data is the main subject of research in data mining, machine learning, and pattern recognition.[2]
Data clustering is a data mining technique that enables the abstraction of large amounts of data by forming meaningful groups or categories of objects, formally known as clusters, such that objects in the same cluster are similar to each other, and those in different clusters are dissimilar. A cluster of objects indicates a level of similarity between objects such that we can consider them to be in the same category, this simplifying our reasoning about them considerably.
It is well known that no clustering method can effectively deal with all kinds of cluster structures and configurations. In fact, the cluster structure produced by a clustering method is sometimes an artifact of the method itself. Combining clusterings invokes multiple clustering algorithms in the clustering process to benefit from each other to achieve global benefit (i.e. they cooperate together to attain better overall clustering quality). Thus, cooperative clustering model achieves synchronous execution of the invoked techniques with no idle time and obtains clustering solutions with better homogeneity than those of the non-cooperative clustering algorithms. The cooperative clustering model is mainly based on four components
- Co-occurred sub-clusters,
- Histogram representation of the pair-wise similarities within sub-clusters,
- The cooperative contingency graph, and
- The coherent merging between the set of histograms.
These components are developed to obtain a cooperative model that is capable of clustering data with better quality than that of the adopted non-cooperative techniques.
See also[edit]
References[edit]
- ↑ Rasha Kashef and Mohamed S. Kamel (2010). "Cooperative Clustering". Pattern Recognition. 43 (6): 2315–2329.
- ↑ Rasha F. Kashef (2008). Cooperative Clustering Model and Its Applications (Thesis). Waterloo, Ontario, Canada.
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