This section gives an overview of the literature related to our work in twofold: the use of ontology in data mining and data warehouse mining.
(1) Use of ontology in data mining
If the concept hierarchy or taxonomy can be viewed as an ontology, then the use of ontologies in data mining can be traced back to 1991 when Nunez used information of the classification hierarchy and attribute processing cost to improve the efficiency of the classification process(Nunez, 1991). Later, Han & Fu (1995) and Srikant & Agrawal (1995) also proposed combining classification hierarchies to mine multilevel association rules and generalized association rules, respectively. Their works were later extended by Chien et al.
(2007), who not only applied classification but also composition hierarchical knowledge to mining fuzzy association rules. These researches, however, concentrated on the design of the algorithms, yet discussion of ontology structure design and its benefit to data mining were not covered. Until recently, research on applying ontology to data mining was exploited by several studies such as, ontology-based induction of rules (Aronis et al., 1996; Taylor et al., 1997), based business understanding (Sharma & Osei-Bryson, 2009), ontology-based post-processing and explanation of association rules (Domingues & Rezende, 2005;
Liao et al., 2009; Marinica et al., 2008; Svatek et al., 2005), ontology-supported selection of classification algorithms (Bernstein et al., 2005; Lin et al., 2006), ontology-guided new
attributes generation from databases (Phillips & Buchanan, 2001), and ontology-based integration and preprocessing of data (Euler & Scholz, 2004; Perez-Rey et al., 2006).
Differing from the above work on dealing with the issue of incorporating ontology in the individual phase of the well known KDD process proposed by Fayyad et al. (1996), there has been work conducted from an integral perspective. For example, Kopanas et al. (2002) pointed out the essence of incorporating ontology (the term domain knowledge is used instead) to the KDD process and demonstrated their viewpoints using a telecommunication customer insolvency case study.Cespivova et al. (2004) conducted a systematic study by discussing the roles of medical domain ontology in each aspect of the KDD process. A similar study was also presented in (Gottgtroy et al., 2004; Kuo et al., 2007). A position paper presented by Charlest et al. (2006) discussed the synergy of combining case based reasoning and ontology in the context of data mining assistance framework, though the issue of realization and implementation was left aside. In 2006, Pan & Pan proposed an ontology supporting data mining from databases. They maintained previous mining results in ontology that can further be applied for incremental association rule mining.
(2) Data warehouse mining
Currently, the research on data warehouse mining is mostly concentrated on data mining from data cubes or multi-dimensional databases. J. Han’s research group pioneered this research subject (Han, 1998; Han et al., 1999). The study conducted by Ester and his colleagues (Ester et al., 1998; Ester & Wittmann, 1998) instead considered the problem of incrementally updating mined patterns from data warehouses. In 2000, Psaila and Lanzi studied multi-level association mining from a primitive data warehouse and proposed a mining algorithm. Since then, substantial works have been devoted to discovering multidimensional association rules from data warehouses (Ng et al., 2002; Chung &
Mangamuri, 2005; Tjioe & Taniar, 2005; Messaoud et al., 2006; Yang et al., 2008).
The research by Priebe & Pernul (2003) first exploited the issues of incorporating ontology into knowledge discovery from data warehouses. In particular, it proposed an intelligent web portal integrating OLAP and information retrieval through ontology, yet it focused on information retrieval issues but not on data mining. Subsequent work on multiple source integration for data warehouse OLAP construction includes Niemi et al. (2007) and Shah et al. (2009). In (Wu et al., 2007), we presented the problems with contemporary association rule mining in data warehousing systems, explained the essence that incorporates
ontologies to resolve the problems, anddemonstrated a preliminary framework.
7. Conclusions
The purpose of data mining is for users to find real and useful knowledge they actually want. In this paper we have shown a data warehouse mining system framework with intelligent assistance incorporating schema ontology, schema constraint ontology, domain ontology and user preference ontology. We have demonstrated the intelligent assistance provided by the mining system in guiding users through the mining processes. This improves the mining effectiveness and efficiency in four aspects as follows. First, the processes of the mining model settings are assisted by intelligent functions, minimizing the possibilities of illegal settings of mining models. Also, appropriate recommendations of the mining model elements are provided while the users are setting the mining model. This avoids execution of ineffective or redundant mining processes and also guides the users through the approaching of the mining models that are closer to their mining intention. Second, with the support of domain ontology, mining rules can be extended and generalized. Third, the information in the domain ontology can be included in the filtering condition to obtain a more specific search space. More precise knowledge can be discovered. Fourth, it provides the system with knowledge browsing capability that a mining model can be examined against the user preference ontology for any duplication or similarities. This saves the system’s resources. In this paper, we have discussed the intelligent assistance in general. A preliminary implementation of this system framework has also been provided to demonstrate the claimed benefits.
The ontologies we have proposed in this paper are implemented in relational table structures. Nevertheless, these ontologies are local to the specific mining system we have proposed. Making them globally sharable is challenging and is an important future work.
Acknowledgements
This work was supported by the National Science Council of R.O.C. under grant NSC 95-2221-E-390-024.
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