Text associative classification approach for mining Arabic data set

2012 ◽  
Author(s):  
Abdullah S. Ghareb ◽  
Abdul Razak Hamdan ◽  
Azuraliza Abu Bakar
Keyword(s):  
Associative Classification ◽  
Data Set ◽  
Classification Approach

scholarly journals A New Classification Approach Based on Multiple Classification Rules

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhongmei Zhou
Keyword(s):  
Classification Rule ◽  
Classification Rules ◽  
Associative Classification ◽  
New Classification ◽  
Class Label ◽  
Rule Based ◽  
Classification Approach ◽  
Minimum Support ◽  
Multiple Classification ◽  
Rule Set

A good classifier can correctly predict new data for which the class label is unknown, so it is important to construct a high accuracy classifier. Hence, classification techniques are much useful in ubiquitous computing. Associative classification achieves higher classification accuracy than some traditional rule-based classification approaches. However, the approach also has two major deficiencies. First, it generates a very large number of association classification rules, especially when the minimum support is set to be low. It is difficult to select a high quality rule set for classification. Second, the accuracy of associative classification depends on the setting of the minimum support and the minimum confidence. In comparison with associative classification, some improved traditional rule-based classification approaches often produce a classification rule set that plays an important role in prediction. Thus, some improved traditional rule-based classification approaches not only achieve better efficiency than associative classification but also get higher accuracy. In this paper, we put forward a new classification approach called CMR (classification based on multiple classification rules). CMR combines the advantages of both associative classification and rule-based classification. Our experimental results show that CMR gets higher accuracy than some traditional rule-based classification methods.

scholarly journals From Individual Graphite Assignment to an Improved Digital Image Analysis of Ductile Iron

2020 ◽  
Vol 14 (4) ◽  
pp. 1090-1104
Author(s):  
J. Friess ◽  
U. Sonntag ◽  
I. Steller ◽  
A. Bührig-Polaczek
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Ductile Iron ◽  
Digital Image Analysis ◽  
Visual Analysis ◽  
Digital Data ◽  
Shape Classification ◽  
Data Set ◽  
Classification Approach ◽  
Graphite Particles

Abstract Since graphite classification by visual analysis exhibits large variations, a more integrative concept of graphite shape classification is required to evaluate the correlations of process, microstructure and properties, and to fulfill customers’ requirements. The automatic digital image analysis is partly based on visual analysis, but it is not thoroughly defined for graphite shape classification. For example, nodules and thereby nodularity are only defined by the shape parameter roundness, although several studies suggest more sophisticated approaches. Within the first of three successive round robin tests, visual assignment for a variety of graphite particles was performed to obtain a universal digital data set of classified graphite particles. For this, the classification approach from standard EN ISO 945-1 was used and extended with degenerated graphite. The assigned particles were evaluated concerning different shape parameters showing that roundness and the assigned minimum limit value of 0.6 are not sufficient to distinguish nodules from less ideal graphite particle shapes. Furthermore, the current classification approach does not represent the full spectrum of graphite morphologies and needs to be extended. The development of a universal hierarchical classification method for nodules and other graphite shapes has been initiated, and the results will contribute to an improved image analysis standard for ductile iron, particularly ISO 945-4.

scholarly journals Reliable Distributed Fuzzy Discretizer for Associative Classification of Big data

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Associative Classification ◽  
Data Set ◽  
Digital World ◽  
Huge Data ◽  
Fuzzy Partitioning ◽  
Discrete Values ◽  
Associative Classifiers

Data Mining is an essential task because the digital world creates huge data daily. Associative classification is one of the data mining task which is used to carry out classification of data, based on the demand of knowledge users. Most of the associative classification algorithms are not able to analyze the big data which are mostly continuous in nature. This leads to the interest of analyzing the existing discretization algorithms which converts continuous data into discrete values and the development of novel discretizer Reliable Distributed Fuzzy Discretizer for big data set. Many discretizers suffer the problem of over splitting the partitions. Our proposed method is implemented in distributed fuzzy environment and aims to avoid over splitting of partitions by introducing a novel stopping criteria. Proposed discretization method is compared with existing distributed fuzzy partitioning method and achieved good accuracy in the performance of associative classifiers.

scholarly journals Rough Sets Base Associative Classification Rules Extraction from Big Data

2019 ◽  
Vol 9 (1) ◽  
pp. 3096-3102
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Extraction Process ◽  
Classification Rules ◽  
Associative Classification ◽  
Mapreduce Framework ◽  
Data Set ◽  
Suggested Approach ◽  
Standard Data ◽  
Rules Extraction

Big Data is a current burning challenge for the data analytics research community. Many conventional data analytics techniques have been extended to the MapReduce framework to process Big Data. But in our literature review, we find that for the MapReduce system there is an absolute lack of rough setbased technique. To facilitate this and recognize the importance of the rule-based classification techniques, we suggest a roughset associative classification rules extraction process for the MapReduce framework. The implementation and evaluation of the Big Data Standard data set demonstrated the efficiency of our suggested approach.

scholarly journals A FUZZY ASSOCIATIVE CLASSIFICATION APPROACH FOR RECOMMENDER SYSTEMS

2012 ◽  
Vol 20 (04) ◽  
pp. 579-617 ◽  
Author(s):  
JOEL PINHO LUCAS ◽  
ANNE LAURENT ◽  
MARÍA N. MORENO ◽  
MAGUELONNE TEISSEIRE
Keyword(s):  
Data Mining ◽  
Recommender Systems ◽  
Real Data ◽  
Associative Classification ◽  
Data Mining Technique ◽  
Classification Approach ◽  
Mining Technique ◽  
Joint Method ◽  
Hybrid Methodology

Despite the existence of different methods, including data mining techniques, available to be used in recommender systems, such systems still contain numerous limitations. They are in a constant need for personalization in order to make effective suggestions and to provide valuable information of items available. A way to reach such personalization is by means of an alternative data mining technique called classification based on association, which uses association rules in a prediction perspective. In this work we propose a hybrid methodology for recommender systems, which uses collaborative filtering and content-based approaches in a joint method taking advantage from the strengths of both approaches. Moreover, we also employ fuzzy logic to enhance recommendations' quality and effectiveness. In order to analyze the behavior of the techniques used in our methodology, we accomplished a case study using real data gathered from two recommender systems. Results revealed that such techniques can be applied effectively in recommender systems, minimizing the effects typical drawbacks they present.

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