Knowledge Discovery in Databases (KDD) is an iterative process that aims at extracting interesting, previously unknown and hidden patterns from huge databases. Data mining is a stage of the entire KDD process that involves applying a particular data mining algorithm to extract an interesting knowledge. One of the very important aspects of any data mining task is the evaluation process of the discovered knowledge. Furthermore, the major issue that faces data mining community is how to use our existing knowledge about domain to evaluate the discovered rules. For the rules to be interesting, the user has to be involved by providing his/her prior knowledge about domain.
Use of objective measures of interestingness in popular data mining algorithms often leads to another mining problem like volume of discovered rules. The reduction in the volume of the discovered rules is desirable in order to improve the efficiency algorithms. Subjective measures of interestingness are required to achieve this.
Clustering algorithms are an important problem in data mining. Massively increasing volume of data in real life databases has motivated researchers to design novel and incremental algorithms for clustering mining. In this thesis, the researcher propose an incremental Clustering mining algorithm that integrates interestingness criteria during the process of building the model. One of the main features of this approach is to capture the user background knowledge, which is monotonically augmented. The proposed algorithm is based on the premise that unless the underlying data generation process has changed dramatically, it is expected that the rules discovered from one set are likely to be similar (in varying degrees) to those discovered from another set. The incremental model that reflects the changing data and the user beliefs is attractive in order to make the overall KDD process more effective and efficient. the researcher tested the proposed framework and experiment with some public medical datasets and found the results quite promising.
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