A Novel Data Stream High Utility Itemset Miner with the Batch Transaction Processing Model
Keywords:
High Utility Itemset, Association Mining, HUI Mining, Data Stream, Batch ModelAbstract
High-utility mining techniques play a significant role in effectively finding the high utility itemsets (HUIs). These techniques aim to find the HUIs based on threshold values of minimum utility. Real-time applications, such as dynamic retail store transactions, continuous web data stream, and item updates in sensor network databases, need dynamic HUI mining techniques. Recently, an incremental mining-based high utility itemset (IM-HUM) was developed to handle the dynamic itemsets in the HUI mining process using incremental schedulers. It was primarily focused on processing HUI based on time frame schedulers rather than considering the amounts or size of items processed during the particular scheduler. It becomes tedious when a reasonable number of items cannot be processed in the prescribed schedule. For this reason, in the proposed work, the reasonable number of items in the data stream is defined by fixing the size of the batch of items instead of considering schedulers. The proposed data stream high utility miner is implemented using a batch model, say (DS-HUI-BM). It is superior to other state-of-the-art HUI mining techniques for both sparse and dense datasets, and the same is illustrated in the experimental section.
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