Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Chengqi Zhang"'
Publikováno v:
Applied Artificial Intelligence. 23:713-737
We propose a simple, novel, and yet effective confidence metric for measuring the interestingness of association rules. Distinguishing from existing confidence measures, our metrics really indicate the positively companionate correlations between fre
Publikováno v:
Applied Artificial Intelligence. 19:677-689
Priori-like algorithms for association rules mining have relied on two user-specified thresholds: minimum support and minimum confidence. There are two significant challenges to applying these algorithms to real-world applications: database-dependent
Publikováno v:
Information Systems. 28:691-707
This paper proposes a new strategy for maintaining association rules in dynamic databases. This method uses weighting technique to highlight new data. Our approach is novel in that recently added transactions are given higher weights. In particular,
Autor:
Shichao Zhang, Chengqi Zhang
Publikováno v:
Applied Artificial Intelligence. 16:333-358
A causal rule between two variables, X M Y, captures the relationship that the presence of X causes the appearance of Y. Because of its usefulness (compared to association rules), techniques for mining causal rules are beginning to be developed. Howe
Publikováno v:
Intelligent Strategies for Pathway Mining ISBN: 9783319041711
MicroRNAs (miRNAs) have been recognized as important regulators of posttranscriptional gene expression. They additionally perform crucial functions in a wide range of biological processes. Many efforts have been made to explore miRNAs, however the in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e173d9f27cbad2e70f01c456ad0ffcf7
https://doi.org/10.1007/978-3-319-04172-8_11
https://doi.org/10.1007/978-3-319-04172-8_11
Bridging rules take the antecedent and action from different conceptual clusters. They are distinguished from association rules (frequent itemsets) because (1) they can be generated by the infrequent itemsets that are pruned in association rule minin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::deed62ff10894b786a5623a69766d7c4
https://hdl.handle.net/10453/22257
https://hdl.handle.net/10453/22257
Enterprise data mining applications often involve complex data such as multiple large heterogeneous data sources, user preferences, and business impact. In such situations, a single method or one-step mining is often limited in discovering informativ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0612637563f6f2fd9e70347d4df720ba
https://hdl.handle.net/10453/15113
https://hdl.handle.net/10453/15113
Publikováno v:
Domain Driven Data Mining ISBN: 9781441957368
The previous chapters have constructed key foundations for domain driven data mining. From this chapter, we start to discuss techniques, means and case studies for implementing domain driven actionable knowledge discoveryAKD and delivery.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b0f7ae983edf0c292c4d620253c0a141
https://doi.org/10.1007/978-1-4419-5737-5_5
https://doi.org/10.1007/978-1-4419-5737-5_5
We design a genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. In this approach, an elaborate encoding method is developed, and the relative confidence is used as the fitness function. With g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14e8cc24784a26602c0b5e83c8630392
https://hdl.handle.net/10453/8957
https://hdl.handle.net/10453/8957
Publikováno v:
Web Intelligence
Traditional sequential pattern mining deals with positive sequential patterns only, that is, only frequent sequential patterns with the appearance of items are discovered. However, it is often interesting in many applications to find frequent sequent