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

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

Association Rule Mining of Relational Data

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.

The Novel Model of Construct Materials Science and Information Based on Association Rule Mining

2013 ◽  
Vol 327 ◽  
pp. 197-200
Author(s):  
Guo Fang Kuang ◽  
Ying Cun Cao
Keyword(s):  
Experimental Data ◽  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Materials Science ◽  
Frequent Itemsets ◽  
Data Sets ◽  
The Novel ◽  
Rule Mining ◽  
Novel Model

The material is used by humans to manufacture the machines, components, devices and other products of substances. Association rules originated in the field of data mining, people use it to find large amounts of data between itemsets of the association. Apriori is a breadth-first algorithm to obtain the support is greater than the minimum support of frequent itemsets by repeatedly scanning the database. This paper presents the construction of materials science and information model based on association rule mining. Experimental data sets prove that the proposed algorithm is effective and reasonable.

scholarly journals AN OPTIMIZED ARM SCHEME FOR DISTINCT NETWORK DATA SET

2015 ◽  
pp. 191-195
Author(s):  
K.GANESH KUMAR ◽  
H.VIGNESH RAMAMOORTHY ◽  
M.PREM KUMAR ◽  
S. SUDHA
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Distributed Databases ◽  
Research Area ◽  
Sequential Algorithm ◽  
Data Sets ◽  
Rule Mining ◽  
Data Set ◽  
Communication Costs

Association rule mining (ARM) discovers correlations between different item sets in a transaction database. It provides important knowledge in business for decision makers. Association rule mining is an active data mining research area and most ARM algorithms cater to a centralized environment. Centralized data mining to discover useful patterns in distributed databases isn't always feasible because merging data sets from different sites incurs huge network communication costs. In this paper, an improved algorithm based on good performance level for data mining is being proposed. In local sites, it runs the application based on the improved LMatrix algorithm, which is used to calculate local support counts. Local Site also finds a center site to manage every message exchanged to obtain all globally frequent item sets. It also reduces the time of scan of partition database by using LMatrix which increases the performance of the algorithm. Therefore, the research is to develop a distributed algorithm for geographically distributed data sets that reduces communication costs, superior running efficiency, and stronger scalability than direct application of a sequential algorithm in distributed databases.

Algorithm of Mining Association Rule Based on Matrix

2014 ◽  
Vol 513-517 ◽  
pp. 786-791
Author(s):  
Zi Zhi Lin ◽  
Si Hui Shu ◽  
Yun Ding
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Itemsets ◽  
Rule Mining ◽  
Rule Based ◽  
Data Mining Algorithms ◽  
Transaction Database ◽  
Temporal And Spatial ◽  
Mining Algorithms ◽  
Mining Association Rule

Association rule mining is one of the most important techniques of data mining. Algorithms based on matrix are efficient due to only scanning the transaction database for one time. In this paper, an algorithm of association rule mining based on the compression matrix is given. It mainly compresses the transaction matrix by integrating various strategies and fleetly finds frequent itemsets. The new algorithm optimizes the known algorithms of mining association rule based on matrix given by some researchers in recent years, which greatly reduces the temporal and spatial complexity, and highly promotes the efficiency of finding frequent itemsets.

scholarly journals Present State-of-The-ART of Dynamic Association Rule Mining Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 309-316
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Healthcare Data ◽  
Data Mining Approach ◽  
Mining Community ◽  
Pattern Growth ◽  
Mining Algorithms

Association Rule Mining (ARM) is a data mining approach for discovering rules that reveal latent associations among persisted entity sets. ARM has many significant applications in the real world such as finding interesting incidents, analyzing stock market data and discovering hidden relationships in healthcare data to mention few. Many algorithms that are efficient to mine association rules are found in the existing literature, apriori-based and Pattern-Growth. Comprehensive understanding of them helps data mining community and its stakeholders to make expert decisions. Dynamic update of association rules that have been discovered already is very challenging due to the fact that the changes are arbitrary and heterogeneous in the kind of operations. When new instances are added to existing dataset that has been subjected to ARM, only those instances are to be used in order to go for incremental mining of rules instead of considering the whole dataset again. Recently some algorithms were developed by researchers especially to achieve incremental ARM. They are broadly grouped into Apriori-based and Pattern-Growth. This paper provides review of Apriori-based and Pattern-Growth techniques that support incremental ARM.

Preserving Privacy in Mining Quantitative Associations Rules

2009 ◽  
Vol 3 (4) ◽  
pp. 1-17 ◽  
Author(s):  
Madhu V. Ahluwalia ◽  
Aryya Gangopadhyay ◽  
Zhiyuan Chen
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Privacy ◽  
State Of The Art ◽  
Academic Community ◽  
Discrete Wavelet ◽  
Rule Mining ◽  
Important Data

Association rule mining is an important data mining method that has been studied extensively by the academic community and has been applied in practice. In the context of association rule mining, the state-of-the-art in privacy preserving data mining provides solutions for categorical and Boolean association rules but not for quantitative association rules. This article fills this gap by describing a method based on discrete wavelet transform (DWT) to protect input data privacy while preserving data mining patterns for association rules. A comparison with an existing kd-tree based transform shows that the DWT-based method fares better in terms of efficiency, preserving patterns, and privacy.

Association Rule Mining of Regional Data

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.

Algorithms of Mining Maximum Frequent Itemsets Based on Compression Matrix

2014 ◽  
Vol 571-572 ◽  
pp. 57-62
Author(s):  
Si Hui Shu ◽  
Zi Zhi Lin
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Recursive Algorithm ◽  
Frequent Itemsets ◽  
Rule Mining ◽  
Temporal Complexity ◽  
Data Mining Algorithms ◽  
Association Data ◽  
Mining Algorithms

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.

Sensitive Items in Privacy Preserving — Association Rule Mining

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.

scholarly journals Data Mining Approach in Retail Knowledge Discovery and Internet Technologies

2018 ◽  
Vol 7 (2) ◽  
pp. 100-105
Author(s):  
Simranjit Kaur ◽  
Seema Baghla
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Large Data ◽  
Internet Technology ◽  
Categorical Variables ◽  
Data Sets ◽  
Rule Mining ◽  
Data Set ◽  
Demographic Profiles

Online shopping has a shopping channel or purchasing various items through online medium. Data mining is defined as a process used to extract usable data from a larger set of any raw data. The data set extraction from the demographic profiles and Questionnaire to investigate the gathered based by association. The method for shopping was totally changed with the happening to internet Technology. Association rule mining is one of the important problems of data mining has been used here. The goal of the association rule mining is to detect relationships or associations between specific values of categorical variables in large data sets.

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