Mining high utility itemsets (HUI) is an interesting research problem in the field of data mining and knowledge discovery. Recently, bio-inspired computing has attracted considerable attention, leading to the development of new algorithms for mining HUIs. These algorithms have shown good performance in terms of efficiency, but are not guaranteed to find all HUIs in a database. That is, the quality is comparatively poor in terms of the number of discovered HUIs. To solve this problem, a new framework based on bio-inspired algorithms is proposed. This approach adjusts the standard roadmap of bio-inspired algorithms by proportionally selecting discovered HUIs as the target values of the next population, rather than maintaining the current optimal values in the next population. Thus, the diversity within populations can be improved. Three new algorithms based on the Bio-HUI framework are developed using the genetic algorithm, particle swarm optimization, and the bat algorithm, respectively. Extensive tests conducted on publicly available datasets show that the proposed algorithms outperform existing state-of-the-art algorithms in terms of efficiency, quality of results and convergence speed.

Mining high utility itemsets is one of the most important research issues in data mining owing to its ability to consider nonbinary frequency values of items in transactions and different profit values for each item. Although a number of relevant approaches have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. In this paper, the authors propose an efficient algorithm, namely BAHUI (Bitmap-based Algorithm for High Utility Itemsets), for mining high utility itemsets with bitmap database representation. In BAHUI, bitmap is used vertically and horizontally. On the one hand, BAHUI exploits a divide-and-conquer approach to visit itemset lattice by using bitmap vertically. On the other hand, BAHUI horizontally uses bitmap to calculate the real utilities of candidates. Using bitmap compression scheme, BAHUI reduces the memory usage and makes use of the efficient bitwise operation. Furthermore, BAHUI only records candidate high utility itemsets with maximal length, and inherits the pruning and searching strategies from maximal itemset mining problem. Extensive experimental results show that the BAHUI algorithm is both efficient and scalable.

The aim of sequential pattern mining (SPM) is to discover potentially useful information from a given sequence. Although various SPM methods have been investigated, most of these focus on mining all of the patterns. However, users sometimes want to mine patterns with the same specific prefix pattern, called co-occurrence pattern. Since sequential rule mining can make better use of the results of SPM, and obtain better recommendation performance, this paper addresses the issue of maximal co-occurrence nonoverlapping sequential rule (MCoR) mining and proposes the MCoR-Miner algorithm. To improve the efficiency of support calculation, MCoR-Miner employs depth-first search and backtracking strategies equipped with an indexing mechanism to avoid the use of sequential searching. To obviate useless support calculations for some sequences, MCoR-Miner adopts a filtering strategy to prune the sequences without the prefix pattern. To reduce the number of candidate patterns, MCoR-Miner applies the frequent item and binomial enumeration tree strategies. To avoid searching for the maximal rules through brute force, MCoR-Miner uses a screening strategy. To validate the performance of MCoR-Miner, eleven competitive algorithms were conducted on eight sequences. Our experimental results showed that MCoR-Miner outperformed other competitive algorithms, and yielded better recommendation performance than frequent co-occurrence pattern mining. All algorithms and datasets can be downloaded from https://github.com/wuc567/Pattern-Mining/tree/master/MCoR-Miner .