Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets - 2015 PROJECT TITLE: Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets - 2015 ABSTRACT: Mining high utility itemsets (HUIs) from databases is a crucial knowledge mining task, which refers to the invention of itemsets with high utilities (e.g. high profits). However, it might present too many HUIs to users, that conjointly degrades the efficiency of the mining process. To achieve high efficiency for the mining task and give a concise mining result to users, we propose a unique framework during this paper for mining closed+ high utility itemsets(CHUIs), that is a compact and lossless illustration of HUIs. We tend to propose three economical algorithms named AprioriCH (Apriori-based algorithm for mining High utility Closed+ itemsets), AprioriHC-D (AprioriHC algorithm with Discarding unpromising and isolated items) and CHUD (Closed+ High Utility Itemset Discovery) to seek out this illustration. Further, a method known as DAHU (Derive All High Utility Itemsets) is proposed to recover all HUIs from the set of CHUIs without accessing the original database. Results on real and synthetic datasets show that the proposed algorithms are terribly efficient which our approaches achieve a huge reduction in the amount of HUIs. Similarly, when all HUIs will be recovered by DAHU, the mixture of CHUD and DAHU outperforms the state-of-the-art algorithms for mining HUIs. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Data Mining Or Data Engineering Projects Efficient Motif Discovery for Large-scale Time Series in Healthcare - 2015 Active Learning for Ranking through Expected Loss Optimization - 2015
