A Strategic approach in Knowledge Mining for Business intelligence using Temporal Data

Abstract: Though Business Intelligence has significantly benefitted with Data Mining which involves evaluating large set of data to find relevant patterns for better understanding and decision making in a particular Business context. In last decade data mining has though addressed many business issues but has also shown some limitations as well. One of the problematic areas of data mining is handling of temporal data, as it is established that transactional data has some seasonal behavior (As Supermarket Sales pattern changes during weekends) and thus need of temporal data mining. And another area of concern is of integrating existing domain knowledge in the mined results. Our paper proposes to address these issues. During past few years many approaches of temporal data mining were put forward with useful applications but they were largely incorporated either on Association Rule Mining or on Classification but We have proposed Temporal aspect integrated with Associative Classification. This integration of Temporal Associative Classification will make the mining process fast with better results, which were otherwise not revealed, which can further enhanced Organizations to have Effective decision making and time based Strategy Planning. Knowledge Management aspect is crucial in any organizational decision making and thus needs to be incorporated in Temporal Data mining process. Data mining acts as a tool for organisation, integration, extracting the data of relevance, error correction aids in a better revenue generation.

Keywords: Temporal Data Mining, Knowledge Management, Temporal predictive association rule