scholarly journals Efficient Rule Generation for Associative Classification

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 299
Author(s):  
Chartwut Thanajiranthorn ◽  
Panida Songram
Keyword(s):  
Data Representation ◽  
Large Set ◽  
Rule Generation ◽  
Associative Classification ◽  
Rule Mining ◽  
Vertical Data ◽  
Multiple Association ◽  
Unseen Data ◽  
Representation Technique ◽  
Memory Resources

Associative classification (AC) is a mining technique that integrates classification and association rule mining to perform classification on unseen data instances. AC is one of the effective classification techniques that applies the generated rules to perform classification. In particular, the number of frequent ruleitems generated by AC is inherently designated by the degree of certain minimum supports. A low minimum support can potentially generate a large set of ruleitems. This can be one of the major drawbacks of AC when some of the ruleitems are not used in the classification stage, and thus (to reduce the rule-mapping time), they are required to be removed from the set. This pruning process can be a computational burden and massively consumes memory resources. In this paper, a new AC algorithm is proposed to directly discover a compact number of efficient rules for classification without the pruning process. A vertical data representation technique is implemented to avoid redundant rule generation and to reduce time used in the mining process. The experimental results show that the proposed algorithm archives in terms of accuracy a number of generated ruleitems, classifier building time, and memory consumption, especially when compared to the well-known algorithms, Classification-based Association (CBA), Classification based on Multiple Association Rules (CMAR), and Fast Associative Classification Algorithm (FACA).

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.

scholarly journals Performance Comparison of Rule Based Classification Algorithms

2011 ◽  
pp. 137-142
Author(s):  
Prafulla Gupta ◽  
Durga Toshniwal
Keyword(s):  
Performance Comparison ◽  
Classification Algorithms ◽  
Rule Generation ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Based ◽  
First Order ◽  
Rule Analysis ◽  
Basic Ideas ◽  
Predictive Rule

Classification based on predictive association rules (CPAR) is a kind of association classification methods which combines the advantages of both associative classification and traditional rule-based classification. For rule generation, CPAR is more efficient than traditional rule-based classification because much repeated calculation is avoided and multiple literals can be selected to generate multiple rules simultaneously. CPAR inherits the basic ideas of FOIL (First Order Inductive Learner) algorithm and PRM (Predictive Rule Mining) algorithm in rule generation. It integrates the features of associative classification in predictive rule analysis. In comparison of FOIL, PRM algorithm usually generates more rules. PRM uses concept of lowering weights rather than removing tuple if tuple is satisfied by the rule. The distinction between CPAR and PRM is that instead of choosing only the attribute that displays the best gain on each iteration CPAR may choose a number of attributes if those attributes have gain close to best gain.

NEW DISCRETE CROW SEARCH ALGORITHM FOR CLASS ASSOCIATION RULE MINING

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Search Algorithm ◽  
Classification Problem ◽  
Discrete Version ◽  
Classification Algorithms ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Based ◽  
Class Association Rule

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

TEXT CLASSIFICATION USING MODIFIED MULTI CLASS ASSOCIATION RULE

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Siti Sakira Kamaruddin ◽  
Yuhanis Yusof ◽  
Husniza Husni ◽  
Mohammad Hayel Al Refai
Keyword(s):  
Text Classification ◽  
Association Rule ◽  
Classification Accuracy ◽  
Classification Problem ◽  
Frequent Pattern ◽  
Associative Classification ◽  
Vertical Data ◽  
Rule Method ◽  
Class Association Rule ◽  
Two Stages

This paper presents text classification using a modified Multi Class Association Rule Method. The method is based on Associative Classification which combines classification with association rule discovery. Although previous work proved that Associative Classification produces better classification accuracy compared to typical classifiers, the study on applying Associative Classification to solve text classification problem are limited due to the common problem of high dimensionality of text data and this will consequently results in exponential number of generated classification rules. To overcome this problem the modified Multi-Class Association Rule Method was enhanced in two stages. In stage one the frequent pattern are represented using a proposed vertical data format to reduce the text dimensionality problem and in stage two the generated rule was pruned using a proposed Partial Rule Match to reduce the number of generated rules. The proposed method was tested on a text classification problem and the result shows that it performed better than the existing method in terms of classification accuracy and number of generated rules.

Support-Less Association Rule Mining Using Tuple Count Cube

2007 ◽  
Vol 06 (04) ◽  
pp. 271-280
Author(s):  
Qin Ding ◽  
William Perrizo
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Data Cube ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
High Confidence ◽  
Support Level ◽  
Vertical Data ◽  
Frequent Item Sets ◽  
High Support

Association rule mining is one of the important tasks in data mining and knowledge discovery (KDD). The traditional task of association rule mining is to find all the rules with high support and high confidence. In some applications, we are interested in finding high confidence rules even though the support may be low. This type of problem differs from the traditional association rule mining problem; hence, it is called support-less association rule mining. Existing algorithms for association rule mining, such as the Apriori algorithm, cannot be used efficiently for support-less association rule mining since those algorithms mostly rely on identifying frequent item-sets with high support. In this paper, we propose a new model to perform support-less association rule mining, i.e., to derive high confidence rules regardless of their support level. A vertical data structure, the Peano Count Tree (P-tree), is used in our model to represent all the information we need. Based on the P-tree structure, we build a special data cube, called the Tuple Count Cube (T-cube), to derive high confidence rules. Data cube operations, such as roll-up, on T-cube, provide efficient ways to calculate the count information needed for support-less association rule mining.

Associative Classification Approaches: Review and Comparison

2014 ◽  
Vol 13 (03) ◽  
pp. 1450027 ◽  
Author(s):  
Neda Abdelhamid ◽  
Fadi Thabtah
Keyword(s):  
Association Rule ◽  
Rule Learning ◽  
Learning Rule ◽  
Data Representation ◽  
Associative Classification ◽  
Data Mining Approach ◽  
Association Rule Discovery ◽  
Multi Class Classification ◽  
Rule Pruning ◽  
New Research

Associative classification (AC) is a promising data mining approach that integrates classification and association rule discovery to build classification models (classifiers). In the last decade, several AC algorithms have been proposed such as Classification based Association (CBA), Classification based on Predicted Association Rule (CPAR), Multi-class Classification using Association Rule (MCAR), Live and Let Live (L3) and others. These algorithms use different procedures for rule learning, rule sorting, rule pruning, classifier building and class allocation for test cases. This paper sheds the light and critically compares common AC algorithms with reference to the abovementioned procedures. Moreover, data representation formats in AC mining are discussed along with potential new research directions.

Efficient Mining of Frequent Itemsets and a Measure of Interest for Association Rule Mining

2004 ◽  
Vol 03 (03) ◽  
pp. 245-257 ◽  
Author(s):  
Kwang-Il Ahn ◽  
Jae-Yearn Kim
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Itemsets ◽  
Second Step ◽  
Rule Mining ◽  
Good Decision ◽  
Data Format ◽  
Vertical Data ◽  
Important Research Topic ◽  
Mining Association Rule

Association rule mining is an important research topic in data mining. Association rule mining consists of two steps: finding frequent itemsets and then extracting interesting rules from the frequent itemsets. In the first step, efficiency is important since discovering frequent itemsets is computationally time consuming. In the second step, unbiased assessment is important for good decision making. In this paper, we deal with both the efficiency of the mining algorithm and the measure of interest of the resulting rules. First, we present an algorithm for finding frequent itemsets that uses a vertical database. We also introduce a modified vertical data format to reduce the size of the database and an itemset reordering strategy to reduce the size of the intermediate tidsets. Second, we present a new measure to evaluate the interest of the resulting association rules. Our performance analysis shows that our proposed algorithm reduces the size of the intermediate tidsets that are generated during the mining process. The smaller tidsets make intersection operations faster. Using our interest-measuring test helps to avoid the discovery of misleading rules.

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