Algorithm of Mining Association Rule Based on Matrix

Association rule mining is one of the most important and well researched techniques of data mining, the key procedure of the association rule mining is to find frequent itemsets , the frequent itemsets are easily obtained by maximum frequent itemsets. so finding maximum frequent itemsets is one of the most important strategies of association data mining. Algorithms of mining maximum frequent itemsets based on compression matrix are introduced in this paper. It mainly obtains all maximum frequent itemsets by simply removing a set of rows and columns of transaction matrix, which is easily programmed recursive algorithm. The new algorithm optimizes the known association rule mining algorithms based on matrix given by some researchers in recent years, which greatly reduces the temporal complexity and spatial complexity, and highly promotes the efficiency of association rule mining.

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Present State-of-The-Art of Association Rule Mining Algorithms

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a2202.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 6398-6405

Keyword(s):

Data Mining ◽

Association Rule ◽

Association Rule Mining ◽

State Of The Art ◽

Synthetic Data ◽

Data Sets ◽

Evolutionary Analysis ◽

Rule Mining ◽

Transaction Database ◽

Mining Algorithms

A Data mining is the method of extracting useful information from various repositories such as Relational Database, Transaction database, spatial database, Temporal and Time-series database, Data Warehouses, World Wide Web. Various functionalities of Data mining include Characterization and Discrimination, Classification and prediction, Association Rule Mining, Cluster analysis, Evolutionary analysis. Association Rule mining is one of the most important techniques of Data Mining, that aims at extracting interesting relationships within the data. In this paper we study various Association Rule mining algorithms, also compare them by using synthetic data sets, and we provide the results obtained from the experimental analysis

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Finding Efficient Positive and Negative Itemsets Using Interestingness Measures

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.36.24133 ◽

2018 ◽

Vol 7 (4.36) ◽

pp. 533

Author(s):

P. Asha ◽

T. Prem Jacob ◽

A. Pravin

Keyword(s):

Association Rule ◽

Association Rule Mining ◽

Data Gathering ◽

Unstructured Data ◽

Rule Mining ◽

Interestingness Measures ◽

Data Mining Algorithms ◽

Data Formats ◽

Mining Algorithms ◽

Rare Itemsets

Currently, data gathering techniques have increased through which unstructured data creeps in, along with well defined data formats. Mining these data and bringing out useful patterns seems difficult. Various data mining algorithms were put forth for this purpose. The associated patterns generated by the association rule mining algorithms are large in number. Every ARM focuses on positive rule mining and very few literature has focussed on rare_itemsets_mining. The work aims at retrieving the rare itemsets that are of most interest to the user by utilizing various interestingness measures. Both positive and negative itemset mining would be focused in this work.

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Association Rule Mining of Relational Data

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch015 ◽

2011 ◽

pp. 87-93

Author(s):

Anne Denton

Keyword(s):

Data Mining ◽

Data Structures ◽

Association Rule ◽

Association Rule Mining ◽

Relational Data ◽

Rule Mining ◽

Data Mining Algorithms ◽

Mining Algorithms ◽

Relational Database Management ◽

Relational Database Management Systems

Most data of practical relevance are structured in more complex ways than is assumed in traditional data mining algorithms, which are based on a single table. The concept of relations allows for discussing many data structures such as trees and graphs. Relational data have much generality and are of significant importance, as demonstrated by the ubiquity of relational database management systems. It is, therefore, not surprising that popular data mining techniques, such as association rule mining, have been generalized to relational data. An important aspect of the generalization process is the identification of challenges that are new to the generalized setting.

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An Efficient Compression Technique for Vertical Mining Methods

Research and Trends in Data Mining Technologies and Applications ◽

10.4018/978-1-59904-271-8.ch006 ◽

2007 ◽

pp. 143-173

Author(s):

Mafruz Ashrafi ◽

David Taniar ◽

Kate Smith

Keyword(s):

Association Rule ◽

Association Rule Mining ◽

Frequent Itemsets ◽

Main Memory ◽

Rule Mining ◽

Compression Technique ◽

Efficient Manner ◽

Handling Technique ◽

Mining Methods ◽

Mining Algorithms

Association rule mining is one of the most widely used data mining techniques. To achieve a better performance, many efficient algorithms have been proposed. Despite these efforts, many of these algorithms require a large amount of main memory to enumerate all frequent itemsets, especially when the dataset is large or the user-specified support is low. Thus, it becomes apparent that we need to have an efficient main memory handling technique, which allows association rule mining algorithms to handle larger datasets in the main memory. To achieve this goal, in this chapter we propose an algorithm for vertical association rule mining that compresses a vertical dataset in an efficient manner, using bit vectors. Our performance evaluations show that the compression ratio attained by our proposed technique is better than those of the other well-known techniques.

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Efficient Mining of Frequent Itemsets and a Measure of Interest for Association Rule Mining

Journal of Information & Knowledge Management ◽

10.1142/s0219649204000869 ◽

2004 ◽

Vol 03 (03) ◽

pp. 245-257 ◽

Cited By ~ 25

Author(s):

Kwang-Il Ahn ◽

Jae-Yearn Kim

Keyword(s):

Association Rule ◽

Association Rule Mining ◽

Frequent Itemsets ◽

Second Step ◽

Rule Mining ◽

Good Decision ◽

Data Format ◽

Vertical Data ◽

Important Research Topic ◽

Mining Association Rule

Association rule mining is an important research topic in data mining. Association rule mining consists of two steps: finding frequent itemsets and then extracting interesting rules from the frequent itemsets. In the first step, efficiency is important since discovering frequent itemsets is computationally time consuming. In the second step, unbiased assessment is important for good decision making. In this paper, we deal with both the efficiency of the mining algorithm and the measure of interest of the resulting rules. First, we present an algorithm for finding frequent itemsets that uses a vertical database. We also introduce a modified vertical data format to reduce the size of the database and an itemset reordering strategy to reduce the size of the intermediate tidsets. Second, we present a new measure to evaluate the interest of the resulting association rules. Our performance analysis shows that our proposed algorithm reduces the size of the intermediate tidsets that are generated during the mining process. The smaller tidsets make intersection operations faster. Using our interest-measuring test helps to avoid the discovery of misleading rules.

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Association Rule Mining of Regional Data

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch014 ◽

2011 ◽

pp. 70-73

Author(s):

Anne Denton ◽

Christopher Besemann

Keyword(s):

Data Mining ◽

Data Structures ◽

Association Rule ◽

Association Rule Mining ◽

Relational Data ◽

Rule Mining ◽

Data Mining Algorithms ◽

Mining Algorithms ◽

Relational Database Management ◽

Relational Database Management Systems

Most data of practical relevance are structured in more complex ways than is assumed in traditional data mining algorithms, which are based on a single table. The concept of relations allows for discussing many data structures such as trees and graphs. Relational data have much generality and are of significant importance, as demonstrated by the ubiquity of relational database management systems. It is, therefore, not surprising that popular data mining techniques, such as association rule mining, have been generalized to relational data. An important aspect of the generalization process is the identification of problems that are new to the generalized setting.

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Sensitive Items in Privacy Preserving — Association Rule Mining

Journal of Information & Knowledge Management ◽

10.1142/s0219649208001932 ◽

2008 ◽

Vol 07 (01) ◽

pp. 31-35

Author(s):

K. Duraiswamy ◽

N. Maheswari

Keyword(s):

Data Mining ◽

Private Information ◽

Association Rule ◽

Association Rule Mining ◽

Privacy Preserving ◽

Data Input ◽

Rule Mining ◽

Privacy Preserving Data Mining ◽

Data Mining Algorithms ◽

Mining Algorithms

Privacy-preserving has recently been proposed in response to the concerns of preserving personal or sensible information derived from data-mining algorithms. For example, through data-mining, sensible information such as private information or patterns may be inferred from non-sensible information or unclassified data. As large repositories of data contain confidential rules that must be protected before published, association rule hiding becomes one of important privacy preserving data-mining problems. There have been two types of privacy concerning data-mining. Output privacy tries to hide the mining results by minimally altering the data. Input privacy tries to manipulate the data so that the mining result is not affected or minimally affected. For some applications certain sensitive predictive rules are hidden that contain given sensitive items. To identify the sensitive items an algorithm SENSITEM is proposed. The results of the work have been given.

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