Modified classic apriori algorithm for association rule mining
In today’s real world environment, information is the most critical element in all aspects of the life. It can be used to perform analysis and it helps to make decision making. But due to large collection of information the analysis and extraction of such useful information is tedious process which...
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| Published in | International journal of engineering & technology (Dubai) Vol. 7; no. 2.21; p. 414 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
20.04.2018
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| Online Access | Get full text |
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| Summary: | In today’s real world environment, information is the most critical element in all aspects of the life. It can be used to perform analysis and it helps to make decision making. But due to large collection of information the analysis and extraction of such useful information is tedious process which will create a major problem. In data mining, Association rules states about associations among the entities of known and unknown group and extracting hidden patterns in the data. Apriori algorithm is used for association rule mining. In this paper, due to limitations in rule condition, the algorithm was extended as new modified classic apriori algorithm which fulfills user stated minimum support and confidence constraints. |
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| ISSN: | 2227-524X 2227-524X |
| DOI: | 10.14419/ijet.v7i2.21.12455 |