Discovering fuzzy inter- and intra-object associations

Research highlights? This paper proposes a new fuzzy data-mining algorithm for extracting interesting knowledge from quantitative transactions stored as object data. ? The proposed algorithm is divided into two main phases, one for intra-object linguistic association rules, and the other for inter-object linguistic association rules. ? The numbers of fuzzy intra-object association rules are usually smaller than those of fuzzy inter-object association rules because the attribute number is less than the item number in real applications. Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Recently, the fuzzy and the object concepts have been very popular and used in a variety of applications, especially for complex data description. This paper thus proposes a new fuzzy data-mining algorithm for extracting interesting knowledge from quantitative transactions stored as object data. Each item itself is thought of as a class, and each item purchased in a transaction is thought of as an instance. Instances with the same class (item name) may have different quantitative attribute values since they may appear in different transactions. The proposed fuzzy algorithm can be divided into two main phases. The first phase is called the fuzzy intra-object mining phase, in which the linguistic large itemsets associated with the same classes (items) but with different attributes are derived. Each linguistic large itemset found in this phase is thought of as a composite item used in phase 2. The second phase is called the fuzzy inter-object mining phase, in which the large itemsets are derived and used to represent the relationship among different kinds of objects. An example is used to illustrate the algorithm. Experimental results are also given to show the effects of the proposed algorithm.