Name
Abstract | ||
|---|---|---|
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 informative knowledge. It would also be very time and space consuming, if not impossible, to join relevant large data sources for mining patterns consisting of multiple aspects of information. It is crucial to develop effective approaches for mining patterns combining necessary information from multiple relevant business lines, catering for real business settings and decision-making actions rather than just providing a single line of patterns. The recent years have seen increasing efforts on mining more informative patterns, e.g., integrating frequent pattern mining with classifications to generate frequent pattern-based classifiers. Rather than presenting a specific algorithm, this paper builds on our existing works and proposes combined mining as a general approach to mining for informative patterns combining components from either multiple data sets or multiple features or by multiple methods on demand. We summarize general frameworks, paradigms, and basic processes for multifeature combined mining, multisource combined mining, and multimethod combined mining. Novel types of combined patterns, such as incremental cluster patterns, can result from such frameworks, which cannot be directly produced by the existing methods. A set of real-world case studies has been conducted to test the frameworks, with some of them briefed in this paper. They identify combined patterns for informing government debt prevention and improving government service objectives, which show the flexibility and instantiation capability of combined mining in discovering informative knowledge in complex data. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TSMCB.2010.2086060 | IEEE Transactions on Systems, Man, and Cybernetics, Part B |
Keywords | Field | DocType |
enterprise data mining,multimethod combined mining,multifeature combined mining,public service data mining,data source,decision making,combined mining,multiple source data mining,discovering informative knowledge,frequent pattern mining,multisource combined mining,mining pattern,informative knowledge discovery,data mining,business data processing,pattern based classifier,complex data,enterprise data mining application,actionable knowledge discovery,informative knowledge,one-step mining,association rule,association rules,artificial intelligence,computer simulation,measurement,government,knowledge discovery,algorithms,distributed database,distributed databases | Data science,Concept mining,Text mining,Data stream mining,Computer science,Complex data type,Association rule learning,Artificial intelligence,Distributed database,Enterprise data management,Machine learning,Government | Journal |
Volume | Issue | ISSN |
41 | 3 | 1941-0492 |
Citations | PageRank | References |
12 | 0.65 | 24 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Longbing Cao | 1 | 2212 | 185.04 |
| Huaifeng Zhang | 2 | 240 | 18.84 |
| Yanchang Zhao | 3 | 233 | 20.01 |
| Dan Luo | 4 | 13 | 1.40 |
| Chengqi Zhang | 5 | 3636 | 274.41 |