An Algorithm of Mining Frequent Itemsets in Pervasive Computing

Author

Teng, Shaohua ; Su, Jiangyu ; Zhang, Wei ; Fu, Xiufen ; Chen, Shuqing

Author_Institution

Guangdong Univ. of Technol., Guangzhou

Volume

2

fYear

2008

fDate

6-8 Oct. 2008

Firstpage

559

Lastpage

563

Abstract

Based on DHP (direct hashing and pruning) algorithm, this paper presents a kind of transaction-marked DHP algorithm (TMDHP for short) to mining frequent itemsets in pervasive computing. Each element of the itemsets and the transaction´s ID will be stored together in the hash-table. Using this method just need to access database once and avoids producing a deal of candidate itemsets. The experiments showed that the performance of the algorithm is better than the conventional apriori algorithm and the DHP algorithm, and has a big advantage for application in pervasive computing.

Keywords

cryptography; data mining; ubiquitous computing; conventional apriori algorithm; direct hashing and pruning algorithm; frequent itemsets mining; pervasive computing; transaction-marked DHP algorithm; Algorithm design and analysis; Approximation algorithms; Data analysis; Data mining; Filtering; Itemsets; Mobile computing; Pervasive computing; Sampling methods; Transaction databases; DHP Algorithm; data mining; frequent itemsets; mining frequent itemset; pervasive computing;

fLanguage

English

Publisher

ieee

Conference_Titel

Pervasive Computing and Applications, 2008. ICPCA 2008. Third International Conference on

Conference_Location

Alexandria

Print_ISBN

978-1-4244-2020-9

Electronic_ISBN

978-1-4244-2021-6

Type

conf

DOI

10.1109/ICPCA.2008.4783675

Filename

4783675