Mining the K-Most Interesting Frequent Patterns Sequentially

Expert Systems with Applications, 2017

Mining maximal frequent patterns (MFPs) is an approach that limits the number of frequent patterns (FPs) to help intelligent systems operate efficiently. Many approaches have been proposed for mining MFPs, but the complexity of the problem is enormous. Therefore, the run time and memory usage are still large. Recently, the N-list structure has been proposed and verified to be very effective for mining FPs, frequent closed patterns, and top-rank-k FPs.

Frequent pattern mining has become an important data mining task and has been a focused theme in data mining research. Frequent patterns are patterns that appear in a data set frequently. Frequent pattern mining searches for recurring relationship in a given data set. Various techniques have been proposed to improve the performance of frequent pattern mining algorithms. This paper presents review of different frequent mining techniques including apriori based algorithms, partition based algorithms, DFS and hybrid algorithms, pattern based algorithms, SQL based algorithms and Incremental apriori based algorithms. A brief description of each technique has been provided. In the last, different frequent pattern mining techniques are compared based on various parameters of importance. Experimental results show that FP- Tree based approach achieves better performance.

Frequent pattern mining is crucial part of association rule mining and other data mining tasks with many practical applications. Current popular algorithms for frequent pattern mining perform differently: some are good for dense databases while the others are ideal for sparse ones. In our previous research, we developed a new frequent pattern mining algorithm named FEM that runs fast on both sparse and dense databases. FEM combines the mining strategies of FP-growth and Eclat and given a user-specified threshold it adapts its mining behaviors to the data characteristics to efficiently find all short and long patterns from different database types. However, for best performance of FEM, an appropriate threshold value used to control the switching between its two mining tasks need to be selected by the user. In this paper, we present DFEM, an improved algorithm of FEM that automatically adopts a runtime dynamic threshold to better fit to the characteristics of the databases. The experi...

IOSR Journal of Computer Engineering, 2013

Efficient algorithm to discover frequent pattern are crucial in data mining research. Finding frequent itemsets is computationally the most expensive step in association rule discovery .To address these issues we discuss popular techniques for finding frequent itemsets in efficient way. In this paper we provide the survey list of existing frequent itemsets mining techniques and proposing new procedure which having some advantages by comparing with the other algorithms.

2008

In this paper, we present a novel frequent pattern mining algorithm, called LPS-Miner, which bases the pattern growth principle and uses two new data structures, LPS-FP-Tree (Light Partial-Support FP-Tree) and LPS-Forest (Light Partial-Support FP-Tree Forest) to present the database. LPS-FP-Tree is a variation of FP-Tree with lighter unidirectional nodes and the mining process depends on the partial-support of the patterns. LPS-Miner adopts partition and divide-and-conquer strategies in maximum, which decomposes the mining task into a set of smaller tasks. The light data structure and efficient memory management mechanism keep the memory usage stable and efficient. Other implementation-based optimizations, such as pruning and outputting-optimization, make the algorithm achieve high efficiency. We test our c++ implementation of this algorithm versus several other algorithms on four datasets. The experimental results show that our algorithm has better space and time efficiency.

Frequent pattern mining is an important chore in the data mining, which reduces the complexity of the data mining task. The usages of frequent patterns in various verticals of the data mining functionalities are discussed in this paper. The gap analysis between the requirements and the existing technology is also analyzed. State of art in the area of frequent pattern mining was thrashed out here. Working mechanisms and the usage of frequent patterns in various practices were conversed in the paper. The core area to be concentrated is the minimal representation, contextual analysis and the dynamic identification of the frequent patterns.

International Journal of Trend in Scientific Research and Development, 2018

Mining of frequent items from a voluminous storage of data is the most favorite topic over the years. Frequent pattern mining has a wide range world applications; market basket analysis is one of them. In this paper, we present an overview of modern frequent pattern mining techniques using data mining algorithms. Frequent pattern mining in data mining takes a lot of data base scans. There computationally expensive task. So still there is a need to update and enhance the existing frequent pattern mining techniques so that we can get the more efficient methods for the same task. In this paper, a study of all the modern and most popular frequent pattern mining technique is also performed.

Frequent pattern mining is one of the most researched areas of data mining and has recently received much attention from the database community. They are proved to be quite useful in the marketing and retail communities as well as other more diverse fields. This survey study aims at giving an overview of the previous researches done in the field of frequent pattern mining algorithms and other related issues available in the literature.

IOSR Journal of Engineering, 2013

Data mining discovers hidden pattern in data sets and association between the patterns. In this association rule mining is one of the technique used to achieve the objective of data mining. These rules are effectively used to uncover unknown relationships producing result that can give us a basis for forecasting and decision making. To discover these rules we have to find out the frequent item sets because these item sets are the building blocks to obtain Association rules with a given confidence and support. In this paper we theoretical analyses the extraction for frequent item sets and compare these algorithm.

2018

Association rule mining is one of the imperative errands in data mining. The undertaking to locate the frequent patterns is assuming a fundamental part in mining associations and numerous other intriguing highlights among the factors in the transactional database. In any case, this assignment is computationally escalated and utilizes a significant extensive measure of memory. There are numerous components that include the working of a frequent pattern mining algorithm. One of the variables that have a noteworthy impact is the attributes of the database being examined. The well known algorithm works distinctively on inadequate and thick database. Two algorithms are being connected to the database as indicated by the data attributes of the dataset. FEM(FP-Tree and Eclat Method) utilizes a settled edge as an exchanging condition between the two mining techniques while DFEM(Dynamic FP-Tree and Eclat Method) applies an edge dynamically at runtime to efficiently fit the qualities of the database amid the mining procedure. The execution