Intension Mining

Knowledge discovery is defined as “the non trivial extraction of implicit, unknown, and potentially useful knowledge of the data” (Fayyad, Piatetsky-Shiapiro, Smyth, & Uthurusamy, 1996, p. 6). According to these principles, the knowledge discovery process (KDP) takes the results just as they come from the data (i.e., the process of extracting tendencies or models of the data), and it carefully and accurately transforms them into useful and understandable information. To consider the discovery of knowledge useful, this knowledge has to be interesting (i.e., it should have a potential value for the user; Han & Kamber, 2001). Current data mining solutions are based on decoupled architectures. Data mining tools assume the data to be already selected, cleaned, and transformed. Large quantities of data are required to provide enough information to derive additional knowledge (Goel, 1999). Because large quantities of data are required, an efficient process becomes essential. With the idea of efficiency, intension mining was born. Gupta, Bhatnagar, and Wasan proposed the architecture and framework. Intension mining arises as a framework that focuses on the user of the current KDP. The basic idea behind the concept of intension mining is to separate the user from the intricacies of the KDP and give him or her a single database management system (DBMS)-like interface to interactively mine for the required kind of knowledge. The user can plan the data mining needs beforehand and input them in the form of knowledge discovery schema (KDS). The system mines knowledge and presents the results in the required format. Intension mining leads to efficiency and makes the whole process more realistic, user-friendly, and, hence, popular (Goel, 1999). As a result, intension mining is a logical extension of incremental mining, with an oriented paradigm to the user, who establishes and conceives the requirements of the mining before the mining begins (Gupta, Bhatnagar, & Wasan, 2000a, 2000d). BACKGROUND