AUTOMATIC HIERARCHICAL E-MAIL CLASSIFICATION USING ASSOCIATION RULES

The explosive growth of on-line communication, in particular e-mail communication, makes it necessary to organize the information for faster and easier processing and searching. Storing e-mail messages into hierarchically organized folders, where each folder corresponds to a separate topic, has proven to be very useful. Previous approaches to this problem use Näıve Bayesor TF-IDF-style classifiers that are based on the unrealistic term independence assumption. These methods are also contextinsensitive in that the meaning of words is independent of presence/absence of other words in the same message. It was shown that text classification methods that deviate from the independence assumption and capture context achieve higher accuracy. In this thesis, we address the problem of term dependence by building an associative classifier called Classification using Cohesion and Multiple Association Rules, or COMAR in short. The problem of context capturing is addressed by looking for phrases in message corpora. Both rules and phrases are generated using an efficient FP-growth-like approach. Since the amount of rules and phrases produced can be very large, we propose two new measures, rule cohesion and phrase cohesion, that possess the anti-monotone property which allows the push of rule and phrase pruning deeply into the process of their generation. This approach to pattern pruning proves to be much more efficient than “generate-and-prune” methods. Both unstructured text attributes and semi-structured non-text attributes, such as senders and recipients, are used for the classification. COMAR classification algorithm uses multiple rules to predict several highest probability topics for each message. Different feature selection and rule ranking methods are compared. Our studies show that the hierarchical associative classifier that utilizes phrases, multiple rules and deep rule pruning and uses biased confidence or rule cohesion for rule ranking achieves higher accuracy and is more efficient than other associative classifiers and is also more accurate than Näıve Bayes.