A global attribute reduction algorithm and its application on partner selection of mobilization alliances

The knowledge reduction function of rough sets theory is specific on discrete data, while most decision tables are comprised of continuous attributes. Therefore a global discretization and attribute reduction algorithm based on clustering and rough sets theory was proposed. Mobilization alliance is important organization of realizing agile mobilization, the basic requirements of virtual enterprises are represented in the index system of partner selection. Under the condition of knowing the potential data of enterprises and decision results from case database, the index system can be reduced effectively through the global discretization and attribute reduction algorithm. An example was proposed to reduce the unnecessary indexes and induce the decision rules of partner selection of mobilization alliances. The results illustrate the feasibility and effectiveness of the algorithm.