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Mining Classification Rules from Datasets with Large Number of Many-Valued Attributes
[chapter]
2000
Lecture Notes in Computer Science
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However, they may fail in discovering effective knowledge when the input dataset consists of a large number of uncorrelated many-valued attributes. ...
Performing a multivariate search leads to a much larger consumption of computation time and memory, this may be prohibitive for large datasets. ...
Thus, they will exhibit similar drawbacks as of C4.5.
Conclusions In this paper we presented an algorithm to mine classification rules from datasets with a large number of many-valued attributes. ...
doi:10.1007/3-540-46439-5_23
fatcat:xec3shz5nna3zfzmxqicid22j4
An Efficient Method for Associative Classification using Jaccard Measure
2019
International journal of recent technology and engineering
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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. ...
The major disadvantage of associative classification is the generation of redundant and weak class association rules. ...
Large size of classifier also decreases the accuracy of classification due to the large number of weak rules or less interesting rules in it. ...
doi:10.35940/ijrte.b1580.0982s1119
fatcat:m4skt6uya5gcren6zyluqvvqea
Fuzzy associative rule-based approach for pattern mining and identification and pattern-based classification
2011
Proceedings of the 20th international conference companion on World wide web - WWW '11
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Associative Classification leverages Association Rule Mining (ARM) to train Rule-based classifiers. The classifiers are built on high quality Association Rules mined from the given dataset. ...
Conventional Associative Classification and Association Rule Mining (ARM) algorithms are inherently designed to work only with binary attributes, and expect any quantitative attributes to be converted ...
Thus, fuzzy versions of Apriori do not perform fast against very large datasets and datasets with a large number of dimensions/attributes. ...
doi:10.1145/1963192.1963347
dblp:conf/www/MangalampalliP11
fatcat:i4atzy334vcxjec2h5vldnjfam
A New Classification Approach Based on Multiple Classification Rules
2014
Mathematical Problems in Engineering
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First, it generates a very large number of association classification rules, especially when the minimum support is set to be low. ...
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. ...
First, it often generates a very large number of association classification rules in association rule mining, especially when the training dataset is large and dense. ...
doi:10.1155/2014/818253
fatcat:ci4m6t37bjf4pldxni5c2q5ojq
Correlation Associative Rule Induction Algorithm Using ACO
2016
Circuits and Systems
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The large data sets may have many null-transactions. A null-transaction is a transaction that does not contain any of the itemsets being examined. ...
Classification and association rule mining are used to take decisions based on relationships between attributes and help decision makers to take correct decisions at right time. ...
The support and confidence measures generate a large number of association rules, most of which are not interesting to the users. The large data sets may have many null-transactions. ...
doi:10.4236/cs.2016.710244
fatcat:lzpyxzdw5ve3zi6cacz7je63pq
Hadoop based Feature Selection and Decision Making Models on Big Data
2016
Indian Journal of Science and Technology
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Methods/Analysis: Hadoop, which is a working model based on the Map-Reduce framework with efficient computation and processing of Big Data. ...
Unlike existing solutions that require a prior knowledge of classification accuracy for various types of data characteristics, which is impossible to obtain in practice. ...
Large data sets are downloaded from the UCI repository, which consists of 41 attributes with 10 decision classes and large number of instances. ...
doi:10.17485/ijst/2016/v9i10/88905
fatcat:2h6kz3u6qzf5xgt2coshk6sgqi
A Classification Technique using Associative Classification
2011
International Journal of Computer Applications
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The performance of the Neural Network Associative Classification system is compared with the previous Classification Based Association on four datasets from UCI machine learning repository. ...
Classification and association rule mining are two basic tasks of Data Mining. Classification rule mining is used to discover a small set of rules in the database to form an accurate classifier. ...
Classification datasets often contain many continuous attributes. Mining of association rules with continuous attributes is still a research issue. ...
doi:10.5120/2430-3268
fatcat:koaysp253re6tjwek4zorysnge
Integrated Associative Classification and Neural Network Model Enhanced by Using a Statistical Approach
2013
International Journal of Data Mining & Knowledge Management Process
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Associative classification is provided with a large number of rules, from which a set of quality rules are chosen to develop an efficient classifier. ...
Many attribute selection measures are used to reduce the number of generated rules. ...
INTRODUCTION Data mining techniques extract hidden predictive information from very large databases. ...
doi:10.5121/ijdkp.2013.3407
fatcat:5ly4mmbywbbpdlyzk7h76bskfi
An Evolutionary Algorithm for Automated Discovery of Small-Disjunct Rules
2012
International Journal of Computer Applications
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In the context of data mining, small disjuncts are rules covering a small number of examples. Due to their nature, small disjuncts are error prone. ...
rules mining. ...
If part of the rule consists of a conjunction conditions on the values of at most n-1 predicting attributes, where n is the number of attributes being mined .Decision D is a single term that contains the ...
doi:10.5120/5547-7615
fatcat:zz4ecdvslbgedef6y7qh2gv3dy
AN ALGORITHM FOR MINING USABLE RULES USING A HOLISTIC SWARM BASED APPROACH
2014
Journal of Computer Science
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It has been applied over four medical datasets to classify patients as fit or unfit. The paper begins with an explanation of rule mining functionality and concept of swarm intelligence. ...
One such problem is to accurately classify patients using rule mining methodology while controlling the size of output rules. ...
If the number of attributes and their values is very large, it leads to a large search space and very big output rule set. ...
doi:10.3844/jcssp.2014.585.592
fatcat:7pghmffyvralrnnkwafgnx2mb4
Analysis of Classification Techniques for Efficient Disease Prediction
2016
International Journal of Computer Applications
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Data mining plays an important role in processing large volumes of data. It refers to the process of obtaining knowledge from raw data. ...
Many researches showed that C4.5 algorithm need to be improvised to maximize accuracy, handle large amounts of data, where C5.0 is the improved version. ...
Classification using C5.0 a) Rule set Initially, 50% of instances were selected from both the datasets as Training dataset It can be used to obtain the classification rule sets. . ...
doi:10.5120/ijca2016912388
fatcat:uuhee4ehyndxpifxjqwk6qlja4
ACN: An associative classifier with negative rules
2008
2008 IEEE International Conference on System of Systems Engineering
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This paper approaches the problem of generating negative rules from a classification perspective, how to generate a sufficient number of high quality negative rules efficiently so that classification accuracy ...
Over the years, a number of associative classifiers based on positive rules have been proposed in literature. ...
Firstly, in classification datasets, typically the number of attributes is not large and each attribute has a small number of possible values. ...
doi:10.1109/sysose.2008.4724163
dblp:conf/sysose/KunduIM08
fatcat:4magb7wzi5abhdias7d2c4mqra
ACN: An Associative Classifier with Negative Rules
2008
2008 11th IEEE International Conference on Computational Science and Engineering
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This paper approaches the problem of generating negative rules from a classification perspective, how to generate a sufficient number of high quality negative rules efficiently so that classification accuracy ...
Over the years, a number of associative classifiers based on positive rules have been proposed in literature. ...
Firstly, in classification datasets, typically the number of attributes is not large and each attribute has a small number of possible values. ...
doi:10.1109/cse.2008.48
dblp:conf/cse/KunduIMB08
fatcat:nnnofm52ije3hjp3unci35rx2a
Gain ratio based fuzzy weighted association rule mining classifier for medical diagnostic interface
2014
Sadhana (Bangalore)
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It used a ranking based weight value to identify the potential attribute. When we take a large number of distinct values, the computation of information gain value is not feasible. ...
Fuzzy association rule mining is wellperformed better than traditional classifiers but it suffers from the exponential growth of the rules produced. ...
The major drawback of using information gain is that it tends to choose attributes with large numbers of distinct values over attributes with fewer values even though the latter is more informative (Asha ...
doi:10.1007/s12046-013-0198-1
fatcat:lbjiozvforfgfe3e4raku2fcra
A New Classification-Rule Pruning Procedure for an Ant Colony Algorithm
[chapter]
2006
Lecture Notes in Computer Science
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This work proposes a new rule pruning procedure for Ant-Miner, an Ant Colony algorithm that discovers classification rules in the context of data mining. ...
The performance of Ant-Miner with the new pruning procedure is evaluated and compared with the performance of the original Ant-Miner across several datasets. ...
where the number of attributes was not very large. ...
doi:10.1007/11740698_3
fatcat:kj5quxbv4bbcrbevso4eytuama
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