efficient mining techniques for transaction handling in high utility itemsets

data mining

Now a day’s users are curious while purchasing any kind of product in market. Sometime time peoples may have confusion regarding product reliability among large set of products which has different brands, Sometimes, they decides specific criteria to purchase a product such as, product quality, quantity, other customers reviews etc. User may also define a threshold value for required product. In the research of data mining “HUI” i.e. high utility mining seems to be efficient for mining min_util itemeset from dataset with user specified threshold. However, HUI is very tedious and inefficient method in which min_util is set too low then utility are generated and the other hand if min_util is set too high then no utility will be found. Top-k high utility mining can be a better solution for such kind of problem due to its efficient scalability on huge datasets. There are two types of technique available namely, TKU and TKO for discovering top-k utility. In the real-world application, some of changes occurred in transactional database in case of insertion or deletion operation. Therefore, a technique is required to maintain and update the high utility itemsets in transactional database with the small number of itemset rescanning.

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