AC-Stream: Associative classification over data streams using multiple class association rules

2013 ◽  
Author(s):  
Bordin Saengthongloun ◽  
Thanapat Kangkachit ◽  
Thanawin Rakthanmanon ◽  
Kitsana Waiyamai
Keyword(s):  
Association Rules ◽  
Data Streams ◽  
Associative Classification ◽  
Multiple Class

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

Finding Context Association Rules over Sensor-Actuator Data Streams

2014 ◽  
Author(s):  
Ho Jin Woo ◽  
Se Jung Shin ◽  
Kil Hong Joo ◽  
Won Suk Lee
Keyword(s):  
Association Rules ◽  
Data Streams ◽  
Sensor Actuator

Efficient Mining of Data Streams Using Associative Classification Approach

2015 ◽  
Vol 25 (03) ◽  
pp. 605-631 ◽  
Author(s):  
Prasanna Lakshmi Kompalli ◽  
Ramesh Kumar Cherku
Keyword(s):  
Data Streams ◽  
Processing Time ◽  
Real Data ◽  
Streaming Data ◽  
Infinite Length ◽  
Associative Classification ◽  
Streaming Algorithm ◽  
Scan Data ◽  
Synthetic Datasets ◽  
And Performance

Data stream associative classification poses many challenges to the data mining community. In this paper, we address four major challenges posed, namely, infinite length, extraction of knowledge with single scan, processing time, and accuracy. Since data streams are infinite in length, it is impractical to store and use all the historical data for training. Mining such streaming data for knowledge acquisition is a unique opportunity and even a tough task. A streaming algorithm must scan data once and extract knowledge. While mining data streams, processing time, and accuracy have become two important aspects. In this paper, we propose PSTMiner which considers the nature of data streams and provides an efficient classifier for predicting the class label of real data streams. It has greater potential when compared with many existing classification techniques. Additionally, we propose a compact novel tree structure called PSTree (Prefix Streaming Tree) for storing data. Extensive experiments conducted on 24 real datasets from UCI repository and synthetic datasets from MOA (Massive Online Analysis) show that PSTMiner is consistent. Empirical results show that performance of PSTMiner is highly competitive in terms of accuracy and performance time when compared with other approaches under windowed streaming model.

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