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.

Mining web access sequences can discover very useful knowledge from web logs with broad applications. By considering non-binary occurrences of web pages as internal utilities in web access sequences, e.g., time spent by each user in a web page, more realistic information can be extracted. However, the existing utility-based approach has many limitations such as considering only forward references of web access sequences, not applicable for incremental mining, suffers in the level-wise candidate generation-and-test methodology, needs several database scans and does not show how to mine web traversal sequences with external utility, i.e., different impacts/significances for different web pages. In this paper, we propose a new approach to solve these problems. Moreover, we propose two novel tree structures, called UWAS-tree (utility-based web access sequence tree), and IUWAS-tree (incremental UWAS tree), for mining web access sequences in static and dynamic databases respectively. Our approach can handle both forward and backward references, static and dynamic data, avoids the level-wise candidate generation-and-test methodology, does not scan databases several times and considers both internal and external utilities of a web page. Extensive performance analyses show that our approach is very efficient for both static and incremental mining of high utility web access sequences.

By considering different weights of the items, weighted frequent pattern (WFP)mining can discover more important knowledge compared to traditional frequent pattern mining. Therefore, WFP mining becomes an important research issue in data mining and knowledge discovery area. However, the existing algorithms cannot be applied for stream data mining because they require multiple database scans. Moreover, they cannot extract the recent change of knowledge in a data stream adaptively. In this paper, we propose a sliding window based novel technique WFPMDS (weighted frequent pattern mining over data streams) using a single scan of data stream to discover important knowledge form the recent data elements. Extensive performance analyses show that our technique is very efficient for WFP mining over data streams.

Recently, a significant number of parallel and distributed algorithms have been proposed to mine frequent patterns (FP) from large and/or distributed databases. Among them parallelization of the FP-growth algorithms using the FP-tree has been proved to be highly efficient. However, the FP-tree-based techniques suffer from two major limitations such as multiple database scans requirement (i.e., high I/O cost) and high inter-processor communications cost (during the mining phase). Therefore, we propose a novel tree structure, called PP-tree (Parallel Pattern tree) that significantly reduces the I/O cost by capturing the database contents with a single scan and facilitates the efficient FP-growth mining on it with reduced inter-processor communication overhead. Our parallel algorithm works independently at each local site and locally generates global frequent patterns which are merged at the final stage. The experimental results reflect that parallel and distributed FP mining with PP-tree outperforms other state-of-the-art algorithms.

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.

Multiple-disk I/O systems (disk arrays) have been an attractive approach to meet high performance I/O demands in data intensive applications such as information retrieval systems. When we partition and distribute files across multiple disks to exploit the potential for I/O parallelism, a balanced I/O workload distribution becomes important for good performance. Naturally, the performance of a parallel information retrieval system using an inverted file structure is affected by the partitioning scheme of the inverted file. In this paper, we propose two different partitioning schemes for an inverted file system for a shared-everything multiprocessor machine with multiple disks. We study the performance of these schemes by simulation under a number of workloads where the term frequencies in the documents are varied, the term frequencies in the queries are varied, the number of disks are varied and the multiprogramming level is varied.>