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Different from full periodic patterns, partial periodic patterns could ignore the occurrence of some events in time positions. In this paper, we have presented a gradually pruning algorithm (GPA) for reducing the number of candidate patterns in the mining process. It is based on the two-phased periodic utility upper-bound (PUUB) model and could avoid information loss. Compared to the original approach...
In the past, the multiple fuzzy frequent pattern tree (MFFP tree) was proposed for extracting multiple fuzzy frequent itemsets from quantitative transactions. It kept the multiple transformed fuzzy regions of an item to form the multiple fuzzy frequent itemsets. In this paper, an incremental algorithm is proposed for efficiently mining multiple fuzzy frequent itemsets based on the FUP concepts and...
In the past, pre-large fast-updated sequential pattern trees (pre-large FUSP tree) were proposed for efficiently mining large sequences for record insertion and deletion, respectively. In this paper, we thus proposed a maintenance approach for efficiently maintaining pre-large FUSP trees and effectively deriving desired large sequences when data in databases are modified. Experimental results also...
The privacy-preserving data mining (PPDM) has become an important issue in recent years. In this paper, we propose a lattice-based approach for modifying original databases in order to hide sensitive itemsets. The lattice structure is built based on the relation of sensitive itemsets. The approach uses the bottom-up deletion strategies to gradually reduce the frequency of sensitive itemsets in the...
The increasing popularity of social networks has generated tremendous amount of data to be exploited for commercial, research and many other valuable applications. However, the release of these data has raised an issue that personal privacy may be breached. Current practices of simply removing all identifiable personal information (such as names and social security numbers) before releasing the data...
Many approaches for preserving association rule privacy, such as association rule mining outsourcing, association rule hiding, and anonymity, have been proposed. In particular, association rule hiding on single transaction table has been well studied. However, hiding multi-relational association rule in data warehouses is not yet investigated. This work presents a novel algorithm to hide predictive...
Current technology for association rules hiding mostly applies to data stored in a single transaction table. This work presents a novel algorithm for hiding sensitive association rules in data warehouses. A data warehouse is typically made up of multiple dimension tables and a fact table as in a star schema. Based on the strategies of reducing the confidence of sensitive association rule and without...
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...
The average utility measure reveals a better utility effect of combining several items than the original utility measure. In this paper, we propose a two-phase average-utility mining algorithm that can incrementally maintain the high average-utility itemsets as a database grows. Based on the concept of the FUP algorithm, the proposed algorithm combines the previously mined information from the original...
The average utility measure is adopted in this paper to reveal a better utility effect of combining several items than the original utility measure. A mining algorithm is then proposed to efficiently find the high average-utility itemsets. It uses the summation of the maximal utility among the items in each transaction including the target itemset as the upper bounds to overestimate the actual average...
In this paper, a new concept of up-to-date patterns is proposed, which is a hybrid of the association rules and temporal mining. An up-to-date pattern is composed of an item set and its up-to-date lifetime, in which the user-defined minimum support threshold must be satisfied. The proposed approach can mine more useful large itemsets than the conventional ones which discover large itemsets valid only...
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