IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES
Association Rule Mining is an important field in knowledge mining that allows the rules of association needed for decision making. Frequent mining of objects presents a difficulty to huge datasets. As the dataset gets bigger and more time and burden to uncover the rules. In this paper, overhead an...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Polish Association for Knowledge Promotion
2021-09-01
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| Series: | Applied Computer Science |
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| Online Access: | http://www.acs.pollub.pl/pdf/v17n3/7.pdf |
| _version_ | 1818986794917560320 |
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| author | Pannangi NARESH R. SUGUNA |
| author_facet | Pannangi NARESH R. SUGUNA |
| author_sort | Pannangi NARESH |
| collection | DOAJ |
| description | Association Rule Mining is an important field in knowledge mining that allows the
rules of association needed for decision making. Frequent mining of objects presents a difficulty to huge datasets. As the dataset gets bigger and more time and burden to uncover the rules. In this paper, overhead and time-consuming overhead reduction techniques with an IPOC (Incremental Pre-ordered code) tree structure were examined. For the frequent usage of database mining items, those techniques require highly qualified data structures. FIN(Frequent itemset-Nodeset) employs a node-set, a unique and new data structure to extract frequently used Items and an IPOC tree to store frequent data progressively. Different methods have been modified to analyze and assess time and memory use in different data sets. The strategies suggested and executed shows increased performance when producing rules, using time and efficiency. |
| first_indexed | 2024-12-20T18:56:28Z |
| format | Article |
| id | doaj.art-18668a819a51492e996122fe66a29ff9 |
| institution | Directory Open Access Journal |
| issn | 1895-3735 2353-6977 |
| language | English |
| last_indexed | 2024-12-20T18:56:28Z |
| publishDate | 2021-09-01 |
| publisher | Polish Association for Knowledge Promotion |
| record_format | Article |
| series | Applied Computer Science |
| spelling | doaj.art-18668a819a51492e996122fe66a29ff92022-12-21T19:29:30ZengPolish Association for Knowledge PromotionApplied Computer Science1895-37352353-69772021-09-01173829110.23743/acs-2021-23IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULESPannangi NARESH0https://orcid.org/0000-0003-2932-6699 R. SUGUNA1https://orcid.org/0000-0002-8930-9092 Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, pannanginaresh@gmail.comVel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, ChennaiAssociation Rule Mining is an important field in knowledge mining that allows the rules of association needed for decision making. Frequent mining of objects presents a difficulty to huge datasets. As the dataset gets bigger and more time and burden to uncover the rules. In this paper, overhead and time-consuming overhead reduction techniques with an IPOC (Incremental Pre-ordered code) tree structure were examined. For the frequent usage of database mining items, those techniques require highly qualified data structures. FIN(Frequent itemset-Nodeset) employs a node-set, a unique and new data structure to extract frequently used Items and an IPOC tree to store frequent data progressively. Different methods have been modified to analyze and assess time and memory use in different data sets. The strategies suggested and executed shows increased performance when producing rules, using time and efficiency.http://www.acs.pollub.pl/pdf/v17n3/7.pdffrequent itemsetnodesetfin and ipoc |
| spellingShingle | Pannangi NARESH R. SUGUNA IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES Applied Computer Science frequent itemset nodeset fin and ipoc |
| title | IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES |
| title_full | IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES |
| title_fullStr | IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES |
| title_full_unstemmed | IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES |
| title_short | IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES |
| title_sort | implementation of dynamic and fast mining algorithms on incremental datasets to discover qualitative rules |
| topic | frequent itemset nodeset fin and ipoc |
| url | http://www.acs.pollub.pl/pdf/v17n3/7.pdf |
| work_keys_str_mv | AT pannanginaresh implementationofdynamicandfastminingalgorithmsonincrementaldatasetstodiscoverqualitativerules AT rsuguna implementationofdynamicandfastminingalgorithmsonincrementaldatasetstodiscoverqualitativerules |