Cooperative Clustering Model and Its Applications
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Data clustering plays an important role in many disciplines, including data mining, machine learning, bioinformatics, pattern recognition, and other fields, where there is a need to learn the inherent grouping structure of data in an unsupervised manner. There are many clustering approaches proposed in the literature with different quality/complexity tradeoffs. Each clustering algorithm works on its domain space with no optimum solution to all datasets of different properties, sizes, structures, and distributions. Challenges in data clustering include, identifying proper number of clusters, scalability of the clustering approach, robustness to noise, tackling distributed datasets, and handling clusters of different configurations. This thesis addresses some of these challenges through cooperation between multiple clustering approaches. We introduce a Cooperative Clustering (CC) 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. The two data structures are designed to find the matching sub-clusters between different clusterings and to obtain the final set of cooperative clusters through a merging process. Obtaining the co-occurred objects from the different clusterings enables the cooperative model to group objects based on a multiple agreement between the invoked clustering techniques. In addition, merging this set of sub-clusters using histograms poses a new trend of grouping objects into more homogenous clusters. The cooperative model is consistent, reusable, and scalable in terms of the number of the adopted clustering approaches. In order to deal with noisy data, a novel Cooperative Clustering Outliers Detection (CCOD) algorithm is implemented through the implication of the cooperation methodology for better detection of outliers in data. The new detection approach is designed in four phases, (1) Global non-cooperative Clustering, (2) Cooperative Clustering, (3) Possible outlier’s Detection, and finally (4) Candidate Outliers Detection. The detection of outliers is established in a bottom-up scenario. The thesis also addresses cooperative clustering in distributed Peer-to-Peer (P2P) networks. Mining large and inherently distributed datasets poses many challenges, one of which is the extraction of a global model as a global summary of the clustering solutions generated from all nodes for the purpose of interpreting the clustering quality of the distributed dataset as if it was located at one node. We developed distributed cooperative model and architecture that work on a two-tier super-peer P2P network. The model is called Distributed Cooperative Clustering in Super-peer P2P Networks (DCCP2P). This model aims at producing one clustering solution across the whole network. It specifically addresses scalability of network size, and consequently the distributed clustering complexity, by modeling the distributed clustering problem as two layers of peer neighborhoods and super-peers. Summarization of the global distributed clusters is achieved through a distributed version of the cooperative clustering model. Three clustering algorithms, k-means (KM), Bisecting k-means (BKM) and Partitioning Around Medoids (PAM) are invoked in the cooperative model. Results on various gene expression and text documents datasets with different properties, configurations and different degree of outliers reveal that: (i) the cooperative clustering model achieves significant improvement in the quality of the clustering solutions compared to that of the non-cooperative individual approaches; (ii) the cooperative detection algorithm discovers the nonconforming objects in data with better accuracy than the contemporary approaches, and (iii) the distributed cooperative model attains the same quality or even better as the centralized approach and achieves decent speedup by increasing number of nodes. The distributed model offers high degree of flexibility, scalability, and interpretability of large distributed repositories. Achieving the same results using current methodologies requires polling the data first to one center location, which is sometimes not feasible.