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It is obvious that the problem of Frequent Itemset Mining (FIM) is very popular in data mining, which generates frequent itemsets from a transaction database. An extension of the frequent itemset mining is High Utility Itemset Mining (HUIM) which identifies itemsets with high utility from the transaction database. This gains popularity in data mining, because it identifies itemsets which have more value but the same was not identified as frequent by Frequent Itemset Mining. HUIM is generally referred to as Utility Mining. The utility of the items is measured based on parameters like cost, profit, quantity or any other measures preferred by the users. Compared to high utility itemsets (HUIs) mining, high average utility itemsets (HAUIs) mining is more precise by considering the number of items in the itemsets. In state-of-the-art algorithms that mines HUIS and HAUIs use a single fixed minimum utility threshold based on which HAUIs are identified. In this paper, the proposed algorithm mines HAUIs from transaction databases using Artificial Fish Swarm Algorithm (AFSA) with computed multiple minimum average utility thresholds. Computing the minimum average utility threshold for each item with the AFSA algorithm outperforms other state-of-the-art HAUI mining algorithms with multiple minimum utility thresholds and user-defined single minimum threshold in terms of number of HAUIs. It is observed that the proposed algorithm outperforms well in terms of execution time, number of candidates generated and memory consumption when compared to the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Mining maximal frequent patterns is significant in many fields, but the mining efficiency is often low. The bottleneck lies in too many candidate subgraphs and extensive subgraph isomorphism tests. In this paper we propose an efficient mining algorithm. There are two key ideas behind the proposed methods. The first is to divide each edge of every certain graph (converted from equivalent uncertain graph) and build search tree, avoiding too many candidate subgraphs. The second is to search the tree built in the first step in order, avoiding extensive subgraph isomorphism tests. The evaluation of our approach demonstrates the significant cost savings with respect to the state-of-the-art approach not only on the real-world datasets as well as on synthetic uncertain graph databases. [ABSTRACT FROM AUTHOR]

In the past, most algorithms proposed for mining association rules handle items with binary values. Transactions with quantitative values are, however, commonly seen in real-world applications. Fuzzy mining algorithms for inducing rules from quantitative databases have thus been developed, most of which are based on the Apriori algorithm. Fuzzy mining is seldom based on frequent pattern (FP) trees because fuzzy-set processing from an FP tree is much more complicated than crisp-set processing. In this paper, a two-phase fuzzy mining approach based on a tree structure to obtain fuzzy frequent itemsets from a quantitative database is proposed. A simple tree structure called the upper-bound fuzzy frequent-pattern (UBFFP) tree is designed. The two-phase fuzzy mining approach can easily derive the upper-bound fuzzy counts of itemsets using the tree. It prunes unpromising itemsets in the first phase, and then finds the actual fuzzy frequent itemsets in the second phase. Experimental results show that the proposed approach has good performance. [ABSTRACT FROM AUTHOR]