By defining the correlativity measure, the problem of incremental discovering association rule is discussed in theory, and the mining association rule and the mining classification rule are combined to research, which establishes the theoretical foundations for researching the problem in detail. The correlativity measure depicts the numeral character of given relation and mutuality set. The conception, the definition and the properties of the proposed correlativity measure, and the relation between support and confidence are analyzed and discussed in detail. The new definition, methods, methods of computing support, and the confidence based on mutuality set are proposed. The incremental computing formulas of support and confidence are given, and incremental theorems of support and confidence are also proved. On the side, the influences of incremental data upon association rules and the confidence are analyzed in detail. The problem of pruning candidate frequent item set based on new support is also discussed. The correlativity measure and its idea proposed in this paper provide a new valuable way for studying a unification method for mining classification rules and associte rules from database.