Abstract
In recent years, the field of data mining is getting more important. The reason is that the growth of data brings a huge amount of hidden knowledge. For efficiently and accurately extracting the knowledge, many data mining techniques are proposed. In the thesis, we focus on two techniques: frequent sequence mining and classification. The main challenge of mining frequent sequences is the high processing cost due to the large amount of data. In this thesis, we propose a novel strategy to find all the frequent sequences without having to compute the support counts of non-frequent sequences. The previous works prune candidate sequences based on the frequent sequences with shorter lengths, while our strategy prunes candidate sequences according to the non-frequent sequences with the same lengths. As a result, our strategy can cooperate with the previous works to achieve a better performance. We then identify three major strategies used in the previous works and combine them with our strategy into an efficient algorithm. The novelty of our algorithm lies in its ability to dynamically switch from a strategy to our new strategy in the mining process for a better performance. Experiment results show that our algorithm outperforms the previous ones under various parameter settings. The accuracy of multimedia data retrieval can be enhanced by a data classification and feedback mechanism. It is known that constructing a classifier for the multimedia data in high dimensional feature space is time-consuming. For supporting user feedbacks immediately, in this thesis we study how to efficiently construct the classifier. Our main idea is to speed up the classifier construction process by employing an indexing strategy. The RCE-network classifier is good for this purpose due to its high accuracy and simple construction process. A new RCE-network construction algorithm which overcomes the defects of the existing algorithms was proposed. Moreover, a pruning method with dimension-independent pruning ability was used to efficiently construct the classifier in the high dimensional feature space. Compared with several existing classification methods, the experiment results show that our method significantly promotes the construction efficiency of the classifier for its online uses.