Notice of Retraction: Application of Classification Algorithm Based on Association Rules in the Labor Market

An Associate Rules Mining Algorithm Based on Artificial Immune Network for SAR Image Segmentation

Mathematical Problems in Engineering ◽

10.1155/2015/839081 ◽

2015 ◽

Vol 2015 ◽

pp. 1-14

Author(s):

Mengling Zhao ◽

Hongwei Liu

Keyword(s):

Association Rules ◽

Large Scale ◽

Classification Algorithm ◽

Data Sets ◽

Immune Network ◽

Artificial Immune ◽

Mining Algorithm ◽

Sar Image Segmentation ◽

Artificial Immune Network ◽

Adaptive Pso

As a computational intelligence method, artificial immune network (AIN) algorithm has been widely applied to pattern recognition and data classification. In the existing artificial immune network algorithms, the calculating affinity for classifying is based on calculating a certain distance, which may lead to some unsatisfactory results in dealing with data with nominal attributes. To overcome the shortcoming, the association rules are introduced into AIN algorithm, and we propose a new classification algorithm an associate rules mining algorithm based on artificial immune network (ARM-AIN). The new method uses the association rules to represent immune cells and mine the best association rules rather than searching optimal clustering centers. The proposed algorithm has been extensively compared with artificial immune network classification (AINC) algorithm, artificial immune network classification algorithm based on self-adaptive PSO (SPSO-AINC), and PSO-AINC over several large-scale data sets, target recognition of remote sensing image, and segmentation of three different SAR images. The result of experiment indicates the superiority of ARM-AIN in classification accuracy and running time.

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A Novel Classification Algorithm Based on Association Rules Mining

Knowledge Acquisition: Approaches, Algorithms and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-642-01715-5_6 ◽

2009 ◽

pp. 61-75 ◽

Cited By ~ 14

Author(s):

Bay Vo ◽

Bac Le

Keyword(s):

Association Rules ◽

Classification Algorithm ◽

Association Rules Mining

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Automatic Classification Algorithm for Multisearch Data Association Rules in Wireless Networks

Wireless Communications and Mobile Computing ◽

10.1155/2021/5591387 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Cailing Li ◽

Wenjun Li

Keyword(s):

Wireless Network ◽

Association Rules ◽

Data Association ◽

Automatic Classification ◽

Classification Algorithm ◽

Fuzzy Classification ◽

Classification Error ◽

Data Generation ◽

High Coverage ◽

Efficient Data

In order to realize efficient data processing in wireless network, this paper designs an automatic classification algorithm of multisearch data association rules in a wireless network. According to the algorithm, starting from the mining of multisearch data association rules, from the discretization of continuous attributes of multisearch data, generation of fuzzy classification rules, and the design of association rule classifier and other aspects, automatic classification is completed by using the mining results. Experimental results show that this algorithm has the advantages of small classification error, good real-time performance, high coverage rate, and high feasibility.

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A Novel Efficient Classification Algorithm Based on Class Association Rules

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.135-136.106 ◽

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.

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