Discovering Informative Association Rules for Associative Classification

2008 ◽  
Author(s):  
Zhitong Su ◽  
Wei Song ◽  
Danyang Cao ◽  
Jinhong Li
Keyword(s):  
Association Rules ◽  
Associative Classification

ASSOCIATIVE CLASSIFICATION OF MAMMOGRAMS BASED ON PARALLEL MINING OF IMAGE BLOCKS

2012 ◽  
Vol 24 (06) ◽  
pp. 513-524
Author(s):  
Mohsen Alavash Shooshtari ◽  
Keivan Maghooli ◽  
Kambiz Badie
Keyword(s):  
Association Rules ◽  
Classification Systems ◽  
Classification Model ◽  
Automated Classification ◽  
Associative Classification ◽  
Classification Problems ◽  
Association Rules Mining ◽  
Parallel Mining ◽  
Transactional Databases ◽  
Unique Decision

One of the main objectives of data mining as a promising multidisciplinary field in computer science is to provide a classification model to be used for decision support purposes. In the medical imaging domain, mammograms classification is a difficult diagnostic task which calls for development of automated classification systems. Associative classification, as a special case of association rules mining, has been adopted in classification problems for years. In this paper, an associative classification framework based on parallel mining of image blocks is proposed to be used for mammograms discrimination. Indeed, association rules mining is applied to a commonly used mammography image database to classify digital mammograms into three categories, namely normal, benign and malign. In order to do so, first images are preprocessed and then features are extracted from non-overlapping image blocks and discretized for rule discovery. Association rules are then discovered through parallel mining of transactional databases which correspond to the image blocks, and finally are used within a unique decision-making scheme to predict the class of unknown samples. Finally, experiments are conducted to assess the effectiveness of the proposed framework. Results show that the proposed framework proved successful in terms of accuracy, precision, and recall, and suggest that the framework could be used as the core of any future associative classifier to support mammograms discrimination.

scholarly journals An Efficient Method for Associative Classification using Jaccard Measure

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3448-3453
Keyword(s):  
Association Rules ◽  
Vital Role ◽  
Classification Methods ◽  
Associative Classification ◽  
Data Mining Technique ◽  
Target Class ◽  
Interestingness Measures ◽  
Mining Technique ◽  
Major Disadvantage ◽  
Traditional Classification

Classification is a data mining technique that categorizes the items in a database to target classes. The aim of classification is to accurately find the target class for each instance of the data. Associative classification is a classification method that uses Class Association Rules for classification. Associative classification is found to be often more accurate than some traditional classification methods. The major disadvantage of associative classification is the generation of redundant and weak class association rules. Weak class association rules results in increase in size and decrease in accuracy of the classifier. This paper proposes an efficient approach to build a compact and accurate classifier by using interestingness measures for pruning rules. Interestingness measures play a vital role in reducing the size and increasing the accuracy of classifier by pruning redundant or weak rules. Rules which are strong are retained and these rules are further used to build the classifier. The source of the data used in this paper is University of California Irvine Machine Learning Repository. The approach proposed in this paper is effective and the results show that the approach can produce a highly compact and accurate classifier

IHAC: Incorporating Heuristics for Efficient Rule Generation & Rule Selection in Associative Classification

2021 ◽  
Vol 20 (01) ◽  
pp. 2150010
Author(s):  
Parashu Ram Pal ◽  
Pankaj Pathak ◽  
Shkurte Luma-Osmani
Keyword(s):  
Association Rules ◽  
Search Space ◽  
Classification Rule ◽  
Rule Generation ◽  
Classification Methods ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Selection ◽  
Speed Up ◽  
Experimental Findings

Associations rule mining along with classification rule mining are both significant techniques of mining of knowledge in the area of knowledge discovery in massive databases stored in different geographic locations of the world. Based on such combination of these two, class association rules for mining or associative classification methods have been generated, which, in far too many cases, showed higher prediction accuracy than platitudinous conventional classifiers. Motivated by the study, in this paper, we proposed a new approach, namely IHAC (Incorporating Heuristics for efficient rule generation & rule selection in Associative Classification). First, it utilises the database to decrease the search space and then explicitly explores the potent class association rules from the optimised database. This also blends rule generation and classifier building to speed up the overall classifier construction cycle. Experimental findings showed that IHAC performs better than any further associative classification methods.

A Novel Efficient Classification Algorithm Based on Class Association Rules

2011 ◽  
Vol 135-136 ◽  
pp. 106-110
Author(s):  
Shou Juan Zhang ◽  
Quan Zhou
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Classification Accuracy ◽  
Classification Algorithm ◽  
Training Phase ◽  
Associative Classification ◽  
Sorting Method ◽  
Classification Technique ◽  
Frequent Items ◽  
Distribution Frequency

A novel classification algorithm based on class association rules is proposed in this paper. Firstly, the algorithm mines frequent items and rules only in one phase. Then, the algorithm ranks rules that pass the support and confidence thresholds using a global sorting method according to a series of parameters, including confidence, support, antecedent cardinality, class distribution frequency, item row order and rule antecedent length. Classifier building is based on rule items that do not overlap in the training phase and rule items that each training instance is covered by only a single rule. Experimental results on the 8 datasets from UCI ML Repository show that the proposed algorithm is highly competitive when compared with the C4.5,CBA,CMAR and CPAR algorithms in terms of classification accuracy and efficiency. This algorithm can offer an available associative classification technique for data mining.

scholarly journals A Review on associative classification for Diabetic Datasets A Simulation Approach

2013 ◽  
Vol 7 (1) ◽  
pp. 533-538
Author(s):  
Deepti Jain ◽  
Divakar Singh
Keyword(s):  
Neural Network ◽  
Association Rules ◽  
Classification System ◽  
Association Rule ◽  
Association Rule Mining ◽  
Review Paper ◽  
Classification Rule ◽  
Associative Classification ◽  
Rule Mining ◽  
The Neural Network

Association rules are used to discover all the interesting relationship in a potentially large database. Association rule mining is used to discover a small set of rules over the database to form more accurate evaluation. They capture all possible rules that explain the presence of some attributes in relation to the presence of other attributes. This review paper aims to study and observe a flexible way, of, mining frequent patterns by extending the idea of the Associative Classification method. For better performance, the Neural Network Association Classification system is also analyzed here to be one of the approaches for building accurate and efficient classifiers. In this review paper, the Neural Network Association Classification system is studied and compared in order to find best possible accurate results. Association rule mining and classification rule mining can be integrated to form a framework called as Associative Classification and these rules are referred as Class Association Rules. This review research paper also analyzes how data mining techniques are used for predicting different types of diseases. This paper reviewed the research papers which mainly concentrated on predicting Diabetes.

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