Paper Title
Dual Mining Approach For Knowledge Discovery In Complex Data

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry as well as media attention. Data mining applications normally involve complex data such as multiple hefty varied data sources, user preferences, and business crash. In such conditions, a specific method or one-step mining is often limited to discovering informative knowledge. It would also be very time plus space consuming, unless impossible, to join relevant large data sources for mining patterns consisting of multiple aspects of information. It is necessary for the future to come up with efficient method for mining patterns combining necessary information from various relevant business lines, catering for real business settings plus decision-making actions rather than just providing a single line of patterns. Sooner than presenting a particular 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 ways on demand. The mechanism describes the paradigms and basic processes for multi feature combined mining, multi source combined mining, and multi method combined mining. Novel types of combined patterns, for example incremental cluster patterns, can result from these frameworks, which cannot be completely produced all the way through existing methods. A set of existent case studies has been performed to test the frameworks, with few of them briefed here. They recognize combined patterns for informing government debt prevention and improving government service objectives, which illustrate the flexibility and instantiation capability of combined mining in discovering informative knowledge in complex data. . Index Terms— Knowledge Discover, Multi feature combined mining, Multi source combined mining, and multi method combined mining.