With the rapid growth of data volume, knowledge acquisition for bigdata has become a new challenge. To address this issue, the hierarchical decision table is defined and implemented in this work. The properties of di...
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ISBN:
(纸本)9781467372220
With the rapid growth of data volume, knowledge acquisition for bigdata has become a new challenge. To address this issue, the hierarchical decision table is defined and implemented in this work. The properties of different hierarchical decision tables are discussed under the different granularity of conditional attributes. A novel knowledge acquisition algorithm for bigdata using MapReduce is proposed. Experimental results demonstrate that the proposed algorithm is able to deal with bigdata and mine hierarchical decision rules under the different granularity.
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