Frequent itemsets in data streams using dynamically generated minimum support

dc.contributor.author	Naik, S.B.
dc.contributor.author	Pawar, J.D.
dc.date.accessioned	2018-11-01T04:17:12Z
dc.date.available	2018-11-01T04:17:12Z
dc.date.issued	2018
dc.identifier.citation	Advances in Intelligent Systems and Computing. Springer, Singapore. 828; 2018; 357-365.	en_US
dc.identifier.uri	https://doi.org/10.1007/978-981-13-1610-4_36
dc.identifier.uri	http://irgu.unigoa.ac.in/drs/handle/unigoa/5487
dc.description.abstract	We have presented an approach that generates frequent itemsets from data stream. The itemsets are compressed and then stored in the memory. The decision to whether or not compress an itemset is based on the utility of the itemset. In this chapter, the utility of an itemset is defined in terms of the amount of memory saved by its compression. Beside this, we have presented an approach to dynamically generate the value of minimum support threshold based on the data in the data streams. It avoids having a fixed minimum support threshold throughout the data stream. Since the value is generated from the latest elements in the data stream, it suits to be an appropriate measure to separate the frequent itemsets from the non-frequent ones.	en_US
dc.publisher	Springer	en_US
dc.subject	Computer Science and Technology	en_US
dc.title	Frequent itemsets in data streams using dynamically generated minimum support	en_US
dc.type	Book chapter	en_US