Association Rule Mining for Suspicious Email Detection: A Data Mining Approach

2007 ◽  
Author(s):  
S. Appavu ◽  
R. Rajaram
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Data Mining Approach

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.

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

scholarly journals ASSOCIATION RULE MINING UNTUK MENINGKATKAN PROMOSI PRODUK ( STUDI KASUS PADA PD. XYZ )

2018 ◽  
Vol 7 (2) ◽  
pp. 284-288
Author(s):  
Doni Winarso ◽  
Anwar Karnaidi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining

Analisis association rule adalah teknik data mining yang digunakan untuk menemukan aturan asosiatif antara suatu kombinasi item. penelitian ini menggunakan algoritma apriori. Dengan  algoritma tersebut dilakukan pencarian  frekuensi dan item barang yang paling sering muncul. hasil dari penelitian in menunjukkan bahwa algoritma apriori  dapat digunakan untuk menganalisis data transaksi sehingga diketahui mana produk yang harus  dipromosikan. Perhitungan metode apriori menghasilkan suatu pola pembelian yang terjadi di PD. XYZ. dengan menganalisis pola tersebut dihasilakn kesimpulan bahwa produk  yang akan dipromosikan yaitu cat tembok ekonomis dan peralatan cat berupa kuas tangan dengan nilai support 11% dan confidence 75% .

Artificial Bee Colony-Based Associative Classifier for Healthcare Data Diagnosis

2021 ◽  
pp. 237-253
Author(s):  
M. Nandhini ◽  
S. N. Sivanandam ◽  
S. Renugadevi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Artificial Bee Colony ◽  
Optimization Technique ◽  
Rule Mining ◽  
Healthcare Data ◽  
Bee Colony ◽  
Small Set ◽  
Significant Class

Data mining is likely to explore hidden patterns from the huge quantity of data and provides a way of analyzing and categorizing the data. Associative classification (AC) is an integration of two data mining tasks, association rule mining, and classification which is used to classify the unknown data. Though association rule mining techniques are successfully utilized to construct classifiers, it lacks in generating a small set of significant class association rules (CARs) to build an accurate associative classifier. In this work, an attempt is made to generate significant CARs using Artificial Bee Colony (ABC) algorithm, an optimization technique to construct an efficient associative classifier. Associative classifier, thus built using ABC discovered CARs achieve high prognostic accurateness and interestingness value. Promising results were provided by the ABC based AC when experiments were conducted using health care datasets from the UCI machine learning repository.

Constraint-Based Association Rule Mining

2011 ◽  
pp. 307-312 ◽  
Author(s):  
Carson Kai-Sang Leung
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Computational Cost ◽  
Knowledge Discovery In Databases ◽  
Rule Mining ◽  
The Subject ◽  
User Focus ◽  
High Computational Cost

The problem of association rule mining was introduced in 1993 (Agrawal et al., 1993). Since then, it has been the subject of numerous studies. Most of these studies focused on either performance issues or functionality issues. The former considered how to compute association rules efficiently, whereas the latter considered what kinds of rules to compute. Examples of the former include the Apriori-based mining framework (Agrawal & Srikant, 1994), its performance enhancements (Park et al., 1997; Leung et al., 2002), and the tree-based mining framework (Han et al., 2000); examples of the latter include extensions of the initial notion of association rules to other rules such as dependence rules (Silverstein et al., 1998) and ratio rules (Korn et al., 1998). In general, most of these studies basically considered the data mining exercise in isolation. They did not explore how data mining can interact with the human user, which is a key component in the broader picture of knowledge discovery in databases. Hence, they provided little or no support for user focus. Consequently, the user usually needs to wait for a long period of time to get numerous association rules, out of which only a small fraction may be interesting to the user. In other words, the user often incurs a high computational cost that is disproportionate to what he wants to get. This calls for constraint-based association rule mining.

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.

On Association Rule Mining for the QSAR Problem

2011 ◽  
pp. 83-86
Author(s):  
Luminita Dumitriu
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Predictive Ability ◽  
Quantitative Structure Activity Relationship ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
Neuro Fuzzy ◽  
The 1960S ◽  
New Compounds

The concept of Quantitative Structure-Activity Relationship (QSAR), introduced by Hansch and co-workers in the 1960s, attempts to discover the relationship between the structure and the activity of chemical compounds (SAR), in order to allow the prediction of the activity of new compounds based on knowledge of their chemical structure alone. These predictions can be achieved by quantifying the SAR. Initially, statistical methods have been applied to solve the QSAR problem. For example, pattern recognition techniques facilitate data dimension reduction and transformation techniques from multiple experiments to the underlying patterns of information. Partial least squares (PLS) is used for performing the same operations on the target properties. The predictive ability of this method can be tested using cross-validation on the test set of compounds. Later, data mining techniques have been considered for this prediction problem. Among data mining techniques, the most popular ones are based on neural networks (Wang, Durst, Eberhart, Boyd, & Ben-Miled, 2004) or on neuro-fuzzy approaches (Neagu, Benfenati, Gini, Mazzatorta, & Roncaglioni, 2002) or on genetic programming (Langdon, &Barrett, 2004). All these approaches predict the activity of a chemical compound, without being able to explain the predicted value. In order to increase the understanding on the prediction process, descriptive data mining techniques have started to be used related to the QSAR problem. These techniques are based on association rule mining. In this chapter, we describe the use of association rule-based approaches related to the QSAR problem.

Association Rule and Quantitative Association Rule Mining among Infrequent Items

2010 ◽  
pp. 15-32 ◽  
Author(s):  
Ling Zhou ◽  
Stephen Yau
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Transactional Databases ◽  
Frequent Items ◽  
Increasing Demand ◽  
Quantitative Association Rule

Association rule mining among frequent items has been extensively studied in data mining research. However, in recent years, there is an increasing demand for mining infrequent items (such as rare but expensive items). Since exploring interesting relationships among infrequent items has not been discussed much in the literature, in this chapter, the authors propose two simple, practical and effective schemes to mine association rules among rare items. Their algorithms can also be applied to frequent items with bounded length. Experiments are performed on the well-known IBM synthetic database. The authors’ schemes compare favorably to Apriori and FP-growth under the situation being evaluated. In addition, they explore quantitative association rule mining in transactional databases among infrequent items by associating quantities of items: some interesting examples are drawn to illustrate the significance of such mining.

Sign in / Sign up

Export Citation Format

Share Document