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Measures of Class Membership in Association Rule Based Classification

Viet Phan-Luong
2009 2009 International Conference on Advanced Information Networking and Applications Workshops  
In this work, we focus on the measures of class membership defined for classifiers based on associative classification rules. We revisit the χ 2 test for defining an effective measure.  ...  For comparison, we adapt the weight of evidence (Wang and Wong TKDE 2003) for a system based on the notions of support and confidence. Some variants of those measures are also defined.  ...  Based on this property, classifiers can be built with only key itemsets.  ... 
doi:10.1109/waina.2009.81 dblp:conf/aina/Phan-Luong09 fatcat:wjqvecsfpjfxlnxphtnv2h5drq

Auto-Label Threshold Generation for Multiple Relational Classifications based on SOM Network

Ram PrakashGangwar, Jitendra Agrawal, Varsha Sharma
2012 International Journal of Computer Applications  
This paper is based on MrCAR (Multi-relational Classification Algorithm) and Kohonen's Self-Organizing Maps (SOM) approach.  ...  Classification and Association rule mining are two basic tasks of Data Mining. Classification rules mining finds rules that partition the data into disjoint sets.  ...  Classification based on multiple relational association rules, also called multiple associative classifications is a technique for building accurate and efficient classifier.  ... 
doi:10.5120/4979-7237 fatcat:w2wbo6lpwbehnpzxsqny44c5yi

A compact and understandable associative classifier based on overall coverage

Jamolbek Mattiev, Branko Kavšek
2020 Procedia Computer Science  
The advantage of our proposed classifier is that it generates reasonably less rules on bigger datasets compared to traditional rule-based classifiers.  ...  The advantage of our proposed classifier is that it generates reasonably less rules on bigger datasets compared to traditional rule-based classifiers.  ...  AC aims to build accurate and efficient classifiers based on association rules.  ... 
doi:10.1016/j.procs.2020.03.050 fatcat:j7ilvxxptnh4lfze7kajbmrfvy

An Enhanced Frequent Pattern Growth Based on MapReduce for Mining Association Rules

Arkan A.G Al-Hamodi, Songfeng Lu, Yahya E.A Al-Salhi
2016 International Journal of Data Mining & Knowledge Management Process  
KEYWORDS Association Rule, frequent pattern, Mapreduce, Hadoop.  ...  Our proposed method implemented the EFP-Growth based on MapReduce framework using Hadoop approach. New method has high achieving performance compared with the basic FP-Growth.  ...  The paper concludes with Section.6. RELATED WORK Association Rule mining one kind of data mining algorithms, it can be classified into two types: FP-Growth algorithm and Apriori algorithm.  ... 
doi:10.5121/ijdkp.2016.6202 fatcat:hxiqomtxwbcrzmgotyy6m66oby

Data mining and the Web

Minos N. Garofalakis, Rajeev Rastogi, S. Seshadri, Kyuseok Shim
1999 Proceedings of the second international workshop on Web information and data management - WIDM '99  
The association rules problem is that of computing all association rules that satisfy user-specified minimum support and minimum confidence constraints.  ...  An association rule has the form ¢ ¡ ¤ £ , where and £ are sets of items or itemsets. Let the support of an itemset be the fraction of database transactions that contain .  ... 
doi:10.1145/319759.319781 dblp:conf/widm/GarofalakisRSS99 fatcat:zttixd2fajcsvbiibc43g563vq

Classification Using Association Rules: Weaknesses and Enhancements [chapter]

Bing Liu, Yiming Ma, Ching-Kian Wong
2001 Data Mining for Scientific and Engineering Applications  
This paper aims to improve such an exhaustive search based classification system CBA (Classification Based on Associations).  ...  In the past few years, extensive research was done in the database community on learning rules using exhaustive search under the name of association rule mining.  ...  LB and CAEP are based on rule aggregation rather than rule selection as in CBA and CBA(2). They also do not combine with other methods. GAC uses a RDBMS system to help build classifiers efficiently.  ... 
doi:10.1007/978-1-4615-1733-7_30 fatcat:7twq55swxnduhfwhbvb4xgxxvi

Coverage-Based Classification Using Association Rule Mining

Jamolbek Mattiev, Branko Kavsek
2020 Applied Sciences  
More precisely, we propose a new associative classifier that selects "strong" class association rules based on overall coverage of the learning set.  ...  Building accurate and compact classifiers in real-world applications is one of the crucial tasks in data mining nowadays.  ...  of the classifier.  ... 
doi:10.3390/app10207013 fatcat:c2zayoyd7bdrzbiqan3azeic4i

Business Prospects Prediction for Waqf Lands Using Naïve Bayes And Apriori Algorithm

Amiq Fahmi, Edi Sugiarto, Agus Winarno
2022 Journal of Information Technology and Computer Science  
A threshold value defined based on a mean value from the classification process by the Naïve Bayes was used to select classification results with a deviation of posterior value, and the value which was  ...  This research aims to build a classifier to predict waqf lands as productive or not productive assets for business prospects.  ...  Table 8 shows 2-itemsets defined based on characteristics A1-A7 using itemsets of attribute A8 as the key.  ... 
doi:10.25126/jitecs.202271351 fatcat:2vijsiy355b4fp5hzug4msxyam

Efficient Rule Generation for Associative Classification

Chartwut Thanajiranthorn, Panida Songram
2020 Algorithms  
well-known algorithms, Classification-based Association (CBA), Classification based on Multiple Association Rules (CMAR), and Fast Associative Classification Algorithm (FACA).  ...  Associative classification (AC) is a mining technique that integrates classification and association rule mining to perform classification on unseen data instances.  ...  A key achievement of the ECARG algorithm is that the technique generates valid rules with 100% confidence to build classifiers.  ... 
doi:10.3390/a13110299 fatcat:g3se7zjovzhwpdcytxthy43rei

Sensing the Web for Induction of Association Rules and their Composition through Ensemble Techniques

Agnese Augello, Ignazio Infantino, Giovanni Pilato, Filippo Vella
2020 Procedia Computer Science  
We propose, starting from traditional algorithms such as FP-Growth and Apriori, the creation of complex association rules through boosting of simpler ones.  ...  We propose, starting from traditional algorithms such as FP-Growth and Apriori, the creation of complex association rules through boosting of simpler ones.  ...  task that discovers the association rules between the found itemsets.  ... 
doi:10.1016/j.procs.2020.02.152 fatcat:f63ivmcdszcavjsrbcyri7iose

Research on the Characteristic Model of Learners in Modern Distance Music Classroom Based on Big Data

Yushan Wang, Lianhong Liu, Jie Liu
2022 Scientific Programming  
Firstly, we improve the algorithm itself; aiming at the problem of too many frequent itemsets, an improved key item extraction algorithm KEFP-growth based on FP-growth is proposed, which ignores the frequent  ...  Association rule algorithm for actor feature model mining.  ...  Among them, association rule mining is the most active and deeply researched field. (1) Association Rule Mining Algorithm.  ... 
doi:10.1155/2022/4684461 fatcat:o3ald6i6h5dzlkshihvnramenu

Hybrid Medical Image Classification Using Association Rule Mining with Decision Tree Algorithm [article]

P. Rajendran, M.Madheswaran
2010 arXiv   pre-print
The major steps involved in the system are: pre-processing, feature extraction, association rule mining and hybrid classifier.  ...  The frequent patterns from the CT scan images are generated by frequent pattern tree (FP-Tree) algorithm that mines the association rules.  ...  BUILDING THE HYBRID CLASSIFIER Hybrid Association Rule with Decision Tree Classification Decision tree based classification methods are widely used in data mining and decision support application.  ... 
arXiv:1001.3503v1 fatcat:v4qbxgpn7ja5hbhjzynvkbr4m4

Fastest association rule mining algorithm predictor (FARM-AP)

Metanat HooshSadat, Hamman W. Samuel, Sonal Patel, Osmar R. Zaïane
2011 Proceedings of The Fourth International C* Conference on Computer Science and Software Engineering - C3S2E '11  
Association rule mining is a particularly well studied field in data mining given its importance as a building block in many data analytics tasks.  ...  on it is own partition.  ...  Given a transactional database to mine for association rules, the best and most efficient association rule mining algorithm varies based on the target dataset.  ... 
doi:10.1145/1992896.1992902 dblp:conf/c3s2e/HooshSadatSPZ11 fatcat:cifn4fwhgzb2fd55nojiu7qndq

A review of associative classification mining

FADI THABTAH
2007 Knowledge engineering review (Print)  
This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria.  ...  Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers  ...  For instance, if general practitioners used their patient data to build a rule-based diagnosis system, they would prefer the resulting number of rules to be small and simple.  ... 
doi:10.1017/s0269888907001026 fatcat:yroumla6sregvefnzpnhwqxlhq

Mining Association rules for Low-Frequency itemsets

Jimmy Ming-Tai Wu, Justin Zhan, Sanket Chobe, Dejing Dou
2018 PLoS ONE  
In this paper, we are integrating low-frequency itemsets with high-frequency itemsets, both having low or high utility, and provide different association rules for this combination of itemsets.  ...  It is very important to consider the pattern of itemsets based on the frequency as well as utility to predict more profitable itemsets.  ...  We classify these itemsets as High Frequency, or Low Frequency itemsets based on the frequency value min_supp of k − itemsets. 3.  ... 
doi:10.1371/journal.pone.0198066 pmid:30036359 pmcid:PMC6056028 fatcat:7rnn5dmz6jgpzpgnzrx7xx54ze
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