Associative pattern mining for supervised learning

The internet era has revolutionized computational sciences and automated data collection techniques, made large amounts o f previously inaccessible data available and, consequently, broadened the scope o f exploratory computing research. As a result, data mining, which is still an emerging field o f research, has gained importance because o f its ability to analyze and discover previously unknown, hidden, and useful knowledge from these large amounts o f data. One aspect o f data mining, known as frequent pattern mining, has recently gained importance due to its ability to find associative relationships among the parts o f data, thereby aiding a type o f supervised learning known as “associative learning”. The purpose o f this dissertation is two-fold: to develop and demonstrate supervised associative learning in non-temporal data for multi-class classification and to develop a new frequent pattern mining algorithm for time varying (temporal) data which alleviates the current issues in analyzing this data for knowledge discovery. In order to use associative relationships for classification, we have to algorithmically learn their discriminatory power. While it is well known that multiple sets o f features work better for classification, we claim that the isomorphic relationships among the features work even better and, therefore, can be used as higher order features. To validate this claim, we exploit these relationships as input features for classification instead o f using the underlying raw features. The next part o f this dissertation focuses on building a new classifier using associative relationships as a basis for the multi-class classification problem. Most o f the existing associative classifiers represent the instances from a class in a row-based format wherein one row represents features o f one instance and extract association rules from the entire dataset. The rules formed in this w ay are known as “class constrained rules,” as they have class labels on the right side o f the rules. We argue that this class constrained representation schema lacks important information that is necessary for multi-class classification. Further, most existing works use either the intra­ class or inter-class importance o f the association rules, both o f which sets o f techniques offer empirical benefits. We hypothesize that both intra-class and inter-class variations are important for fast and accurate multi-class classification. We also present a novel weighted association rule-based classification mechanism that uses frequent relationships among raw features from an instance as the basis for classifying the instance into one o f the many classes. The relationships are weighted according to both their intra-class and inter-class importance. The final part o f this dissertation concentrates on mining time varying data. This problem is known as “ inter-transaction association rule mining” in the data-mining field. Most o f the existing work transforms the time varying data into a static format and then use multiple scans over the new data to extract patterns. We present a unique indexbased algorithmic framework for inter-transaction association rule mining. Our proposed technique requires only one scan o f the original database. Further, the proposed technique can also provide the location information o f each extracted pattern. We use mathematical induction to prove that the new representation scheme captures all underlying frequent relationships. APPROVAL FOR SCHOLARLY DISSEM INATION The author grants to the Prescott Memorial Library o f Louisiana Tech University the right to reproduce, by appropriate methods, upon request, any or all portions o f this Dissertation. It is understood that “proper request” consists o f the agreement, on the part o f the requesting party, that said reproduction is for his personal use and that subsequent reproduction will not occur without written approval o f the author o f this Dissertation. Further, any portions o f the Dissertation used in books, papers, and other works must be appropriately referenced to this Dissertation. Finally, the author o f this Dissertation reserves the right to publish freely, in the literature, at any time, any or all portions o f this Dissertation.