An Improved Distributed Lazy Associative Classification Algorithm

doi:10.3969/j.issn.1006-2475.2015.08.024

Abstract

Abstract: Distributed lazy associative classification algorithm (DLAC) refers to a lazy associative classification algorithm using distributed association rules mining. The existing DLAC algorithm has two main problems: one is the inefficiency of classifying multiple test samples; the other is that projection operation is not distributed. Hence, this paper proposed an improved distributed lazy associative classification algorithm—PDLAC algorithm. Firstly, it clustered the test samples using KMeans method， secondly, judged whether it satisfied the aggregating condition or not for each clustered test samples, if it satisfied, aggregated the clustered test samples, if not, let each of the clustered test samples to be one clustered test sample. Then, it executed distributed projection and mined association rules using C-DMA algorithm. Finally, it constructed classifier to classify one or more test sample at the same time. Experiments were conducted with setting the degree of parallelism to 15. The time consumption of PDLAC algorithm was far less than DLAC algorithm, and its performance was much better as the number of testing samples increased. The test results show that PDLAC algorithm is a good solution to both two problems mentioned above.
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Key words: aggregate method, distributed projection, distributed associative rules mining, lazy method, associative classification

CLC Number:

TP312

YANG Hao-min, MA Chao, WU Hai-yan. An Improved Distributed Lazy Associative Classification Algorithm[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.08.024.

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