Efficient Parallel Associative Classification Based On Rules Memoization

Associative classification refers to a class of algorithms that is very efficient in classification problems. Data in such domain are multidimensional, with data instances represented as points of a fixed-length attribute space, and are exploited from two large sets: training and testing datasets. Models, known as classifiers, are mined in the training set by class association rules and are used in eager and lazy strategies for labeling test data instances. Because test data instances are independent and evaluated by sophisticated and high costly computations, an expressive overlap among similar data instances may be introduced. To overcome such drawback, we propose a parallel and high-performance associative classification based on a lazy strategy, which partial computations of similar data instances are cached and shared efficiently. In this sense, a PageRank-driven similarity metric is introduced to reorder computations by affinity, improving frequent-demanded association rules memoization in typical cache strategies. The experiments results show that our similarity-based metric maximizes the reuse of rules cached and, consequently, improve application performance, with gains up to 60% in execution time and 40% higher cache hit rate, mainly in limited cache space conditions.