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 (IHUPL-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 (IHUPTF-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 (IHUPTWU-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.

Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases

High utility itemsets refer to the sets of items with high utility like profit in a database, and efficient mining of high utility itemsets plays a crucial role in many real-life applications and is an important research issue in data mining area. To identify high utility itemsets, most existing algorithms first generate candidate itemsets by overestimating their utilities, and subsequently compute the exact utilities of these candidates. These algorithms incur the problem that a very large number of candidates are generated, but most of the candidates are found out to be not high utility after their exact utilities are computed. In this paper, we propose an algorithm, called HUI-Miner (High Utility Itemset Miner), for high utility itemset mining. HUI-Miner uses a novel structure, called utility-list, to store both the utility information about an itemset and the heuristic information for pruning the search space of HUI-Miner. By avoiding the costly generation and utility computation of numerous candidate itemsets, HUI-Miner can efficiently mine high utility itemsets from the utility-lists constructed from a mined database. We compared HUI-Miner with the state-of-the-art algorithms on various databases, and experimental results show that HUI-Miner outperforms these algorithms in terms of both running time and memory consumption.

https://www.philippe-fournier-viger.com/spmf/huiminer.pdf

Mining high utility itemsets without candidate generation

Efficient algorithms for mining frequent itemsets are crucial for mining association rules as well as for many other data mining tasks. Methods for mining frequent itemsets have been implemented using a prefix-tree structure, known as an FP-tree, for storing compressed information about frequent itemsets. Numerous experimental results have demonstrated that these algorithms perform extremely well. In this paper, we present a novel FP-array technique that greatly reduces the need to traverse FP-trees, thus obtaining significantly improved performance for FP-tree-based algorithms. Our technique works especially well for sparse data sets. Furthermore, we present new algorithms for mining all, maximal, and closed frequent itemsets. Our algorithms use the FP-tree data structure in combination with the FP-array technique efficiently and incorporate various optimization techniques. We also present experimental results comparing our methods with existing algorithms. The results show that our methods are the fastest for many cases. Even though the algorithms consume much memory when the data sets are sparse, they are still the fastest ones when the minimum support is low. Moreover, they are always among the fastest algorithms and consume less memory than other methods when the data sets are dense.

/pdf/fast-algorithms-for-frequent-itemset-mining-using-fp-trees-527tbzax6o.pdf

Fast algorithms for frequent itemset mining using FP-trees

We present SPMF, an open-source data mining library offering implementations of more than 55 data mining algorithms. SPMF is a cross-platform library implemented in Java, specialized for discovering patterns in transaction and sequence databases such as frequent itemsets, association rules and sequential patterns. The source code can be integrated in other Java programs. Moreover, SPMF offers a command line interface and a simple graphical interface for quick testing. The source code is available under the GNU General Public License, version 3. The website of the project offers several resources such as documentation with examples of how to run each algorithm, a developer's guide, performance comparisons of algorithms, data sets, an active forum, a FAQ and a mailing list.

/pdf/spmf-a-java-open-source-pattern-mining-library-2zp3jh4x3c.pdf

SPMF: a Java open-source pattern mining library

In this paper we present an efficient, scalable and general algorithm for performing set joins on predicates involving various similarity measures like intersect size, Jaccard-coefficient, cosine similarity, and edit-distance. This expands the existing suite of algorithms for set joins on simpler predicates such as, set containment, equality and non-zero overlap. We start with a basic inverted index based probing method and add a sequence of optimizations that result in one to two orders of magnitude improvement in running time. The algorithm folds in a data partitioning strategy that can work efficiently with an index compressed to fit in any available amount of main memory. The optimizations used in our algorithm generalize to several weighted and unweighted measures of partial word overlap between sets.

/pdf/efficient-set-joins-on-similarity-predicates-1q880dxe7b.pdf

Efficient set joins on similarity predicates

Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns.

In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern-fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent-pattern mining methods.

/pdf/mining-frequent-patterns-without-candidate-generation-a-59tf5v6g2k.pdf

Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

This invention provides methods apparatus and data structures useful for mining databases for frequent items. The invention uses a frequent pattern tree to represent the contents of a database in a manner which is conducive to data mining. The frequent pattern tree tends to be smaller than the original database. A frequent pattern tree can be mined recursively. The frequent pattern tree and associated methods and apparatus of this invention is relatively fast, efficient and scalable and can be used to mine both long and short frequent patterns.

Methods and system for mining frequent patterns

Performance benchmarking has played an important role in the research and development in relational DBMS, object-relational DBMS, data warehouse systems, etc. We believe that benchmarking data mining algorithms is a long overdue task, and it will play an important role in the research and development of data mining systems as well.Frequent pattern mining forms a core component in mining associations, correlations, sequential patterns, partial periodicity, etc., which are of great potential value in applications. There have been a lot of methods proposed and developed for efficient frequent pattern mining in various kinds of databases, including transaction databases, time-series databases, etc. However, so far there is no serious performance benchmarking study of different frequent pattern mining methods.To facilitate an analytical comparison of different frequent mining methods, we have constructed an open test bed for performance study of a set of recently developed, popularly used methods for mining frequent patterns in transaction databases and mining sequential patterns in sequence databases, with different data characteristics. The testbed consists of the following components.A synthetic data generator, which can generate large sets of synthetic data in various kinds of data distributions. A few large data sets from real world applications will also be provided.A good set of typical frequent pattern mining methods, ranging from classical algorithms to recent studies. The method are grouped into three classes: frequent pattern mining, max-pattern mining, and sequential pattern mining. For frequent pattern mining, we will demonstrate Apriori, hashing, partitioning, sampling, TreeProjection, and FP-growth. For maximal pattern mining, we will demonstrate MaxMiner, TreeProjection, and FP-growth-max. For sequential pattern mining, we will demonstrate GSP and FreeSpan.A set of performance curves. These algorithms their running speeds, scalabilities, bottlenecks, and performance on different data distributions, will be compared and demonstrated upon request. Some performance curves from our pre-conference experimental evaluations will also be shown.An open testbed. Our goal is to construct an extensible test bed which integrates the above components and supports an open-ended testing service. Researchers can upload the object codes of their mining algorithms, and run them in the test bed using these data sets. The architecture is shown in Figure 1.This testbed is our first step towards benchmarking data mining algorithms. By doing so, performance of different algorithms can be reported consistently, on the same platform, and in the same environment. After the demo, we plan to make the testbed available on the WWW so that it may, hopefully, benefit further research and development of efficient data mining methods.

Runying Mao

Papers

Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

Methods and system for mining frequent patterns

Towards data mining benchmarking: a test bed for performance study of frequent pattern mining