Structural advances for pattern discovery in multi-relational databases

With ever-growing storage needs and drift towards very large relational storage settings, multi-relational data mining has become a prominent and pertinent field for discovering unique and interesting relational patterns. As a consequence, a whole suite of multi-relational data mining techniques is being developed. These techniques may either be extensions to the already existing single-table mining techniques or may be developed from scratch. For the traditionalists, single-table mining algorithms can be used to work on multi-relational settings by making inelegant and time consuming joins of all target relations. However, complex relational patterns cannot be expressed in a single-table format and thus, cannot be discovered. This work presents a new multi-relational frequent pattern mining algorithm termed Multi-Relational Frequent Pattern Growth (MRFP Growth). MRFP Growth is capable of mining multiple relations, linked with referential integrity, for frequent patterns that satisfy a user specified support threshold. Empirical results on MRFP Growth performance and its comparison with the state-of-the-art multirelational data mining algorithms like WARMR and Decentralized Apriori are discussed at length. MRFP Growth scores over the latter two techniques in number of patterns generated and speed. The realm of multi-relational clustering is also explored in this thesis. A multi-Relational Item Clustering approach based on Hypergraphs (RICH) is proposed. Experimentally RICH combined with MRFP Growth proves to be a competitive approach for clustering multi-relational data. The performance and iii quality of clusters generated by RICH are compared with other clustering algorithms. Finally, the thesis demonstrates the applied utility of the theoretical implications of the above mentioned algorithms in an application framework for auto-annotation of images in an image database. The system is called CoMMA which stands for Combining Multi-relational Multimedia for Associations

Relational clustering models for knowledge discovery and recommender systems

Cluster analysis is a fundamental research field in Knowledge Discovery and Data Mining (KDD). It aims at partitioning a given dataset into some homogeneous clusters so as to reflect the natural hidden data structure. Various heuristic or statistical approaches have been developed for analyzing propositional datasets. Nevertheless, in relational clustering the existence of multi-type relationships will greatly degrade the performance of traditional clustering algorithms. This issue motivates us to find more effective algorithms to conduct the cluster analysis upon relational datasets. In this thesis we comprehensively study the idea of Representative Objects for approximating data distribution and then design a multi-phase clustering framework for analyzing relational datasets with high effectiveness and efficiency. The second task considered in this thesis is to provide some better data models for people as well as machines to browse and navigate a dataset. The hierarchical taxonomy is widely used for this purpose. Compared with manually created taxonomies, automatically derived ones are more appealing because of their low creation/maintenance cost and high scalability. Up to now, the taxonomy generation techniques are mainly used to organize document corpus. We investigate the possibility of utilizing them upon relational datasets and then propose some algorithmic improvements. Another non-trivial problem is how to assign suitable labels for the taxonomic nodes so as to credibly summarize the content of each node. Unfortunately, this field has not been investigated sufficiently to the best of our knowledge, and so we attempt to fill the gap by proposing some novel approaches. The final goal of our cluster analysis and taxonomy generation techniques is to improve the scalability of recommender systems that are developed to tackle the problem of information overload. Recent research in recommender systems integrates the exploitation of domain knowledge to improve the recommendation quality, which however reduces the scalability of the whole system at the same time. We address this issue by applying the automatically derived taxonomy to preserve the pair-wise similarities between items, and then modeling the user visits by another hierarchical structure. Experimental results show that the computational complexity of the recommendation procedure can be greatly reduced and thus the system scalability be improved

Relational clustering models for knowledge discovery and recommender systems

Cluster analysis is a fundamental research field in Knowledge Discovery and Data Mining (KDD). It aims at partitioning a given dataset into some homogeneous clusters so as to reflect the natural hidden data structure. Various heuristic or statistical approaches have been developed for analyzing propositional datasets. Nevertheless, in relational clustering the existence of multi-type relationships will greatly degrade the performance of traditional clustering algorithms. This issue motivates us to find more effective algorithms to conduct the cluster analysis upon relational datasets. In this thesis we comprehensively study the idea of Representative Objects for approximating data distribution and then design a multi-phase clustering framework for analyzing relational datasets with high effectiveness and efficiency. The second task considered in this thesis is to provide some better data models for people as well as machines to browse and navigate a dataset. The hierarchical taxonomy is widely used for this purpose. Compared with manually created taxonomies, automatically derived ones are more appealing because of their low creation/maintenance cost and high scalability. Up to now, the taxonomy generation techniques are mainly used to organize document corpus. We investigate the possibility of utilizing them upon relational datasets and then propose some algorithmic improvements. Another non-trivial problem is how to assign suitable labels for the taxonomic nodes so as to credibly summarize the content of each node. Unfortunately, this field has not been investigated sufficiently to the best of our knowledge, and so we attempt to fill the gap by proposing some novel approaches. The final goal of our cluster analysis and taxonomy generation techniques is to improve the scalability of recommender systems that are developed to tackle the problem of information overload. Recent research in recommender systems integrates the exploitation of domain knowledge to improve the recommendation quality, which however reduces the scalability of the whole system at the same time. We address this issue by applying the automatically derived taxonomy to preserve the pair-wise similarities between items, and then modeling the user visits by another hierarchical structure. Experimental results show that the computational complexity of the recommendation procedure can be greatly reduced and thus the system scalability be improved.EThOS - Electronic Theses Online ServiceUniversity of WarwickUniversity of Warwick. Dept. of Computer ScienceGBUnited Kingdo