A hypergraph is a complex data structure capable of expressing associations among any number of data entities. Overcoming the limitations of traditional graphs, hypergraphs are useful to model real-life problems. Frequent pattern mining is one of the most popular problems in data mining with a lot of applications. To the best of our knowledge, there exists no flexible pattern mining framework for hypergraph databases decomposing associations among data entities. In this article, we propose a flexible and complete framework for mining frequent patterns from a collection of hypergraphs. To discover more interesting patterns beyond the traditional frequent patterns, we propose frameworks for weighted and uncertain hypergraph mining also. We develop three algorithms for mining frequent, weighted, and uncertain hypergraph patterns efficiently by introducing a canonical labeling technique for isomorphic hypergraphs. Extensive experiments have been conducted on real-life hypergraph databases to show both the effectiveness and efficiency of our proposed frameworks and algorithms.

The share measure of item sets has been proposed to discover useful knowledge about numerical values associated with items in a transaction database. Therefore, share-frequent pattern mining problem becomes a very important research issue in data mining. However, the existing algorithms of share-frequent pattern mining are based on static databases. Moreover, they are not suitable for interactive mining. In this paper, we propose a novel tree structure IncrShrFP-Tree (Incremental Share-Frequent Pattern Tree) for incremental and interactive share-frequent pattern mining. It is effective for incremental and interactive mining to utilize the previous tree structure and to use the previous mining results when a database is updated or a minimum support threshold is changed. It needs maximum two database scans to calculate the resultant share-frequent patterns in incremental databases. Extensive performance analyses show that our method is very efficient for incremental and interactive share-frequent pattern mining.

Recently proposed regular pattern mining provides an effective technique to find patterns occurring at regular interval in a static database. However, the occurrence characteristic of patterns may change significantly with the update of database. Therefore, this paper proposes the Incremental Regular Pattern Tree (IncRT) and a pattern growth mining technique to find regular patterns on incremental transactional databases. Experiment results show the effectiveness of the proposed method.

Recently, high utility pattern (HUP) mining is one of the most important research issues in data mining due to its ability to consider the nonbinary frequency values of items in transactions and different profit values for every item. On the other hand, incremental and interactive data mining provide the ability to use previous data structures and mining results in order to reduce unnecessary calculations when a database is updated, or when the minimum threshold is changed. In this paper, we propose three novel tree structures to efficiently perform incremental and interactive HUP mining. The first tree structure, Incremental HUP Lexicographic Tree ({\rm IHUP}_{{\rm {L}}}-Tree), is arranged according to an item's lexicographic order. It can capture the incremental data without any restructuring operation. The second tree structure is the IHUP Transaction Frequency Tree ({\rm IHUP}_{{\rm {TF}}}-Tree), which obtains a compact size by arranging items according to their transaction frequency (descending order). To reduce the mining time, the third tree, IHUP-Transaction-Weighted Utilization Tree ({\rm IHUP}_{{\rm {TWU}}}-Tree) is designed based on the TWU value of items in descending order. Extensive performance analyses show that our tree structures are very efficient and scalable for incremental and interactive HUP mining.

This paper proposes a prefix-tree structure, called CPS-tree (Compact Pattern Stream tree) that efficiently discovers the exact set of recent frequent patterns from high-speed data stream. The CPS-tree introduces the concept of dynamic tree restructuring technique in handling stream data that allows it to achieve highly compact frequency-descending tree structure at runtime and facilitates an efficient FP-growth-based [1] mining technique.