RETRACTED ARTICLE: An improved memory adaptive up-growth to mine high utility itemsets from large transaction databases

Abstract

High utility itemset (HUI) mining identifies an interesting pattern from transactional databases by taking into consider the utility of each in the transaction for instance, margins or profits. Many candidates are generated for HUIs which reduces the mining performance. Frequent pattern-growth (FP-Growth) algorithm was widely used to discover the frequent itemsets using FP-Tree. But, UP-Tree was constructed to store high utility item set. The mining of UP-Tree by FP-Growth extracts high utility itemset and generates too many candidates. So, UP-Growth and UP-Growth+ was proposed to shorten the candidate itemsets. In UP-Growth, two tactics such as discarding local unpromising items (DLU) and decreasing local node (DLN) were used in FP-Growth. In UP-Growth+, two strategies such as DLU and its estimated node utilities (DNU) and DLN tools for the nodes of internal UP-Tree by evaluated the descendant nodes (DNN) were incorporated in FP-Growth. However, the UP-Growth and UP-Growth+ has the problem of poor spatial and temporal localities. Initially, the UP-Tree is created in available main memory then extended to secondary memory when the transaction is large. The storage of data structure in secondary memory and accessibility is not clearly derived in existing algorithms. In this paper, the spatial locality issues of UP-Growth and UP-Growth+ is solved by rearranging nodes of UP-Tree in a depth first manner and temporal locality issues of UP-Growth and UP-Growth+ is solved by page blocking technique which reorganizes the execution part of UP-Growth and UP-Growth+. The computational part is rearranged. In addition to, the memory management strategy is introduced to minimize the space requirement of high utility itemsets. Thus the proposed Improved Adaptive UP-Growth (IAUP-Growth) and Improved Adaptive UP-Growth+ (IAUP-Growth+) overcomes the spatial and temporal locality problem and effectively reduces the memory usage.