Now showing items 1-5 of 5
Comparison of dynamic itemset mining algorithms for multiple support thresholds
(Association for Computing Machinery, 2017-07)
Mining1 frequent itemsets is an important part of association rule mining process. Handling dynamic aspect of databases and multiple support threshold requirements of items are two important challenges of frequent itemset ...
Ontology supported policy modeling in opinion mining process
In e-Society the spreading services offered by Social Web has changed the way of communication and cooperation among citizens, policy-makers, governance bodies and civil society actors. One of the main goals of policymakers ...
Survey: Running and comparing stream clustering algorithms
(CEUR Workshop Proceedings, 2018)
Recently, clustering data streams have become an incredibly important research area for knowledge discovery as applications produce more and more unstoppable streaming data. In this paper we introduce clustering, streams ...
A relativistic opinion mining approach to detect factual or opinionated news sources
The credibility of news cannot be isolated from that of its source. Further, it is mainly associated with a news source’s trustworthiness and expertise. In an effort to measure the trustworthiness of a news source, the ...
Comparison of two association rule mining algorithms without candidate generation
Association rule mining techniques play an important role in data mining research where the aim is to find interesting correlations among sets of items in databases. Although the Apriori algorithm of association rule mining ...