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Utility mining is proposed to consider additional measures, such as profits or costs according to user preference. In the past, a two-phase mining algorithm was proposed for fast discovering high utility itemsets from databases. In this paper, an incremental mining algorithm to efficiently update high utility itemsets is proposed for record insertion. Experimental results also show that the proposed...
Traditionally, the utility of an itemset is the summation of the utilities of the itemset in transactions regardless of its length. The average utility measure was then proposed to reveal a better utility effect of combining several items than the utility measure. In this paper, a maintenance algorithm for high average-utility itemsets is proposed for reducing execution time when records are deleted...
In this paper, we design a new kind of patterns, named high transaction-weighted utility itemsets, which considers not only individual profits and quantities of the items in a transaction, but also the contribution of each transaction in a database. We also propose a two-phased mining algorithm to discover high transaction-weighted utility itemsets. The experimental results on synthetic datasets show...
Data mining technology can help extract useful knowledge from large data sets. The process of data collection and data dissemination may, however, result in an inherent risk of privacy threats. Some sensitive or private information about individuals, businesses and organizations has to be suppressed before it is shared or published. The privacy-preserving data mining (PPDM) has thus become an important...
We propose here an efficient data mining algorithm to hide collaborative recommendation association rules when the database is updated, i.e., when a new data set is added to the original database. For a given predicted item, a collaborative recommendation association rule set [10] is the smallest association rule set that makes the same recommendation as the entire association rule set by confidence...
Data mining plays a central role in knowledge discovery. It involves applying specific algorithms to extract patterns or rules from data sets in a particular representation. Many researchers in database and machine-learning fields are interested in this new research topic since it offers opportunities to discover useful information and important relevant patterns in large databases, thus helping decision-makers...
In this paper, maintenance based we modify the FUFP-tree on the concept of pre-large itemsets for efficiently handling record modification. The proposed approach can achieve a good execution time for tree maintenance especially when each time a small number of records are modified. Experimental results show that the proposed pre-FUFP modification algorithm has a good performance for handling updated...
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