| Cluster analysis is one of the most important and useful data mining techniques, and there are many applications of cluster analysis in pattern extraction, information retrieval, summarization, compression and other areas. The focus of this thesis is on clustering categorical and sequence data. Clustering categorical and sequence data is much more challenging than clustering numeric data because there is no inherently meaningful measure of similarity between the categorical objects and sequences. In this thesis, we design novel efficient and effective clustering algorithms for clustering categorical data and sequence respectively, and we perform extensive experiments to demonstrate the superior performance of our proposed algorithm. We also explore the extent to which the use of the proposed clustering algorithms can help to solve the personal bankruptcy prediction problem.;The prior knowledge about the data can be incorporated into the clustering process, which is known as semi-supervised clustering, to produce considerable improvement in learning accuracy. In this thesis, we view semi-supervised clustering of categorical data as an optimization problem with extra instance-level constraints, and propose a systematic and fully automated approach to guide the optimization process to a better solution in terms of satisfying the constraints, which would also be beneficial to the unconstrained objects. The proposed semi-supervised divisive hierarchical clustering algorithm for categorical data, named SDHCC, is parameter-free, fully automatic and effective in taking advantage of instance-level constraint background knowledge to improve the quality of the resultant dendrogram.;Many existing sequence clustering algorithms rely on a pair-wise measure of similarity between sequences. Usually, such a measure is effective if there are significantly informative patterns in the sequences. However, it is difficult to define a meaningful pair-wise similarity measure if sequences are short and contain noise. In this thesis, we circumvent the obstacle of defining the pairwise similarity by defining the similarity between an individual sequence and a set of sequences. Based on the new similarity measure, which is based on the conditional probability distribution (CPD) model, we design a novel model-based K-means clustering algorithm for sequence clustering, which works in a similar way to the traditional K-means on vectorial data.;Finally, we develop a personal bankruptcy prediction system whose predictors are mainly the bankruptcy features discovered by the clustering techniques proposed in this thesis. The mined bankruptcy features are represented in low-dimensional vector space. From the new feature space, which can be extended with some existing prediction-capable features (e.g., credit score), a support vector machine (SVM) classifier is built to combine these mined and already existing features. Our system is readily comprehensible and demonstrates promising prediction performance.;Clustering categorical data poses two challenges: defining an inherently meaningful similarity measure, and effectively dealing with clusters which are often embedded in different subspaces. In this thesis, we view the task of clustering categorical data from an optimization perspective and propose a novel objective function. Based on the new formulation, we design a divisive hierarchical clustering algorithm for categorical data, named DHCC. In the bisection procedure of DHCC, the initialization of the splitting is based on multiple correspondence analysis (MCA). We devise a strategy for dealing with the key issue in the divisive approach, namely, when to terminate the splitting process. The proposed algorithm is parameter-free, independent of the order in which the data is processed, scalable to large data sets and capable of seamlessly discovering clusters embedded in subspaces. |
