| Summary: | 博士 === 國立中央大學 === 資訊管理研究所 === 99 === Discovering hidden or unknown knowledge is the major theme of most data mining studies. In this dissertation, we propose a new approach to discover conjecturable rules, which categorize observations of a data set into classes of similar attribute values instead of classes of crisp labels. The proposed approach is developed based on the two most developed data mining techniques: Classification and Clustering.
Classification is the problem of identifying the sub-population to which new observations belong. The result is decided according to a set of rules which discovered from a training set of data of observations whose sub-population is known. The technique is known as supervised learning, i.e. pre-defined labels are necessary for the process. The result is a set of rules which are able to predict which label a new observation is belonged to. However, when there is no label existed in the dataset, this technique fails to apply. On the other hand, Clustering is the process of grouping a set of objects into classes of similar objects. No pre-defined label is necessary for the process. It is known as unsupervised learning. Yet no any rule is preserved after the process for future prediction.
The object of this dissertation is to discover conjecturable rules from those datasets which do not have any predefined class label. Furthermore, the technique extends our two previous studies with fuzzy concept and outliers handling. Thus recessive conjecturable rules can be discovered as well as the accuracy is improved. The proposed technique covers the convenience of unsupervised learning as well as the ability of prediction of decision trees. The experiment results show that our proposed approach is capable to discover conjecturable rules as well as recessive rules. Sensitivity analysis is also given for practitioners’ reference.
|
|---|
