arules - A Computational Environment for Mining Association Rules and Frequent Item Sets

Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The package also includes interfaces to two fast mining algorithms, the popular C implementations of Apriori and Eclat by Christian Borgelt. These algorithms can be used to mine frequent itemsets, maximal frequent itemsets, closed frequent itemsets and association rules.

Comparison of different algorithms for exploting the hidden trends in data sources

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2003Includes bibliographical references (leaves: 92-97)Text in English; Abstract: Turkish and English97 leavesThe growth of large-scale transactional databases, time-series databases and other kinds of databases has been giving rise to the development of several efficient algorithms that cope with the computationally expensive task of association rule mining.In this study, different algorithms, Apriori, FP-tree and CHARM, for exploiting the hidden trends such as frequent itemsets, frequent patterns, closed frequent itemsets respectively, were discussed and their performances were evaluated. The perfomances of the algorithms were measured at different support levels, and the algorithms were tested on different data sets (on both synthetic and real data sets). The algorihms were compared according to their, data preparation performances, mining performance, run time performances and knowledge extraction capabilities.The Apriori algorithm is the most prevalent algorithm of association rule mining which makes multiple passes over the database aiming at finding the set of frequent itemsets for each level. The FP-Tree algorithm is a scalable algorithm which finds the crucial information as regards the complete set of prefix paths, conditional pattern bases and frequent patterns by using a compact FP-Tree based mining method. The CHARM is a novel algorithm which brings remarkable improvements over existing association rule mining algorithms by proving the fact that mining the set of closed frequent itemsets is adequate instead of mining the set of all frequent itemsets.Related to our experimental results, we conclude that the Apriori algorithm demonstrates a good performance on sparse data sets. The Fp-tree algorithm extracts less association in comparison to Apriori, however it is completelty a feasable solution that facilitates mining dense data sets at low support levels. On the other hand, the CHARM algorithm is an appropriate algorithm for mining closed frequent itemsets (a substantial portion of frequent itemsets) on both sparse and dense data sets even at low levels of support

Efficiently mining frequent itemsets from very large databases

Efficient algorithms for mining frequent itemsets are crucial for mining association rules and for other data mining tasks. Methods for mining frequent itemsets and for iceberg data cube computation have been implemented using a prefix-tree structure, known as a FP-tree, for storing compressed frequency information. Numerous experimental results have demonstrated that these algorithms perform extremely well. In this thesis 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. The technique works especially well for sparse datasets. We then present new algorithms for mining all frequent itemsets, maximal frequent itemsets, and closed frequent item-sets. The algorithms use the FP-tree data structure in combination with the FP-array technique efficiently, and incorporate various optimization techniques. In the algorithm for mining maximal frequent itemsets, a variant FP-tree data structure, called a MFI-tree, and an efficient maximality-checking approach are used. Another variant FP-tree data structure, called a CFI-tree, and an efficient closedness-testing approach are also given in the algorithm for mining closed frequent itemsets. Experimental results show that our methods outperform the existing methods in not only the speed of the algorithms, but also their memory consumption and their scalability. We also notice that most algorithms for mining frequent itemsets assume that the main memory is large enough for the data structures used in the mining, and very few efficient algorithms deal with the cases when the database is very large or the minimum support is very low. We thus investigate approaches to mining frequent itemsets when data structures are too large to fit in main memory. Several divide-and-conquer algorithms are presented for mining from disks. Many novel techniques are introduced. Experimental results show that the techniques reduce the required disk accesses by orders of magnitude, and enable truly scalable data mining

Mining Big Sources using Efficient Data Mining Algorithms

Abstract: Data mining algorithms are widely used in the real world application in order to discover knowledge from large data sources. These algorithms work on historical data to analyze data in order to bring about trends or patterns. Association rule mining or frequent item set mining is very useful in applications like inductive databases, query expansion and others. A frequent itemset is the itemset when a set of records are repeated for specified number of times in a given dataset. When such frequent itemset is no present in other frequent itemset, it is named as maximal itemset. When it is not as part of other itemset, them it is called closed itemset. These itemsets are used to extract patterns or trends in the real world applications that support in decision making. Recently Uno et al. proposed data mining algorithms to discover maximal itemsets, closed itemsets and frequent itemsets. In this paper we practically explore those algorithms. We implement them in a prototype application and the empirical results reveal that they are very useful for many data mining solutions

Towards scalable algorithm for closed itemset mining in high-dimensional data

Mining frequent itemsets from large dataset has a major drawback in which the explosive number of itemsets requires additional mining process which might filter the interesting ones. Therefore, as the solution, the concept of closed frequent itemset was introduced that is lossless and condensed representation of all the frequent itemsets and their corresponding supports. Unfortunately, many algorithms are not memory-efficient since it requires the storage of closed itemsets in main memory for duplication checks. This paper presents BFF, a scalable algorithm for discovering closed frequent itemsets from high-dimensional data. Unlike many well-known algorithms, BFF traverses the search tree in breadth-first manner resulted to a minimum use of memory and less running time. The tests conducted on a number of microarray datasets show that the performance of this algorithm improved significantly as the support threshold decreases which is crucial in generating more interesting rules

A Fast Algorithm For Data Mining

In the past few years, there has been a keen interest in mining frequent itemsets in large data repositories. Frequent itemsets correspond to the set of items that occur frequently in transactions in a database. Several novel algorithms have been developed recently to mine closed frequent itemsets - these itemsets are a subset of the frequent itemsets. These algorithms are of practical value: they can be applied to real-world applications to extract patterns of interest in data repositories. However, prior to using an algorithm in practice, it is necessary to know its performance as well implementation issues. In this project, we address such a need for the algorithm “Using Attribute Value Lattice to Find Frequent Itemsets” that was developed by Lin et. al. We clarify some aspects of the algorithm, develop an implementation of the algorithm, and present the results of a performance study. In our experiments we find that the running time of the algorithm for certain input datasets grows exponentially. To address this problem, we develop a novel procedure for binning the data. Our results show that with binned data, the running time of the algorithm grows linearly. This allows one to obtain trends for the dataset

CICLAD: A Fast and Memory-efficient Closed Itemset Miner for Streams

Mining association rules from data streams is a challenging task due to the (typically) limited resources available vs. the large size of the result. Frequent closed itemsets (FCI) enable an efficient first step, yet current FCI stream miners are not optimal on resource consumption, e.g. they store a large number of extra itemsets at an additional cost. In a search for a better storage-efficiency trade-off, we designed Ciclad,an intersection-based sliding-window FCI miner. Leveraging in-depth insights into FCI evolution, it combines minimal storage with quick access. Experimental results indicate Ciclad's memory imprint is much lower and its performances globally better than competitor methods.Comment: KDD2