Call For Paper Volume:4 Issue:10 Oct'2017 |

TREE BASED PATTERN AND EVENT DISCOVERY SCHEME

Publication Date : 16/01/2016



Author(s) :

Ms. N.M. Indumathi, ME., , Ms. C. Nithya, ME., .


Volume/Issue :
Volume 3
,
Issue 1
(01 - 2016)



Abstract :

Abstract  Frequent and infrequent itemsets are discovered using the association rule mining methods. Weighted rule mining methods are used to identify the rules with attribute weight values. Rare patterns are discovered using the Infrequent Weighted Itemset (IWI) mining mechanism. Weight and frequency values are analyzed in the Infrequent Weighted Itemset mining process. Two IWI support measures are used in the system. Minimum cost function is used in the IWI-support-min. Maximum cost function is used in the IWI-support-max measure estimation process. Infrequent weighted itemsets are discovered using two algorithms. They are Infrequent Weighted Itemset Miner (IWI Miner) and Minimal Infrequent Weighted Itemset Miner (MIWI Miner). IWI miner uses the IWI support with Max measure value. IWI support Min measure is used in MIWI miner algorithm. IWI Miner and MIWI Miner are Frequent Pattern (FP) Growth-like mining algorithms. Frequent Pattern (FP) tree construction and regressive itemset mining in the FP-tree index tasks are carried out in the IWI mining process. Event discovery is carried out using the IWI mining scheme. Accuracy is improved with Aggregation functions. Optimal cost functions are applied in the mining process. Data usefulness analysis is integrated with the infrequent itemset mining operations.


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TREE BASED PATTERN AND EVENT DISCOVERY SCHEME

January 12, 2016