Search Results - (( associative classification problem algorithm ) OR ( java application mining algorithm ))
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Direct approach for mining association rules from structured XML data
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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Attribute related methods for improvement of ID3 Algorithm in classification of data: A review
Published 2020“…Decision tree is an important method in data mining to solve the classification problems. There are several learning algorithms to implement the decision tree but the most commonly-used is ID3 algorithm. …”
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Accelerating learning performance of back propagation algorithm by using adaptive gain together with adaptive momentum and adaptive learning rate on classification problems
Published 2011“…However, the most serious problem associated with the BP is local minima problem and slow convergence speeds. …”
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Evaluation and optimization of frequent association rule based classification
Published 2014“…In this paper, a systematic way to evaluate the association rules discovered from frequent itemset mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriated sequence of usage is presented. …”
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Mining Sequential Patterns Using I-PrefixSpan
Published 2007“…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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Conference or Workshop Item -
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A web-based implementation of k-means algorithms
Published 2022“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Final Year Project / Dissertation / Thesis -
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Evaluation and optimization of frequent, closed and maximal association rule based classification
Published 2014“…Real world applications of association rule mining have well-known problems of discovering a large number of rules, many of which are not interesting or useful for the application at hand.The algorithms for closed and maximal item sets mining significantly reduce the volume of rules discovered and complexity associated with the task, but the implications of their use and important differences with respect to the generalization power, precision and recall when used in the classification problem have not been examined.In this paper, we present a systematic evaluation of the association rules discovered from frequent, closed and maximal item set mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriate sequence of usage.The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided as a whole and w.r.t individual classes. …”
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An efficient likelihood-based modulation classification algorithm for multiple-input multiple-output systems
Published 2016“…To overcome the problems associated with the existing likelihood-based MC algorithms, a new algorithm is developed in this paper. …”
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Mining Sequential Patterns using I-PrefixSpan
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Citation Index Journal -
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Multi-label learning based on positive label correlations using predictive apriori
Published 2019“…The ignorance of the correlations among labels in the transformation step has caused limited exploitation of the captured correlations in discovering the applicability of Associative Classification (AC) in Multi-label Classification(MLC). …”
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Thesis -
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An enhanced feature selection technique for classification of group based holy Quran verses
Published 2018“…This thesis is about proposing an enhanced feature selection technique for text classification applications. Text classification problem is primarily applied in document labeling. …”
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Irrelevant feature and rule removal for structural associative classification
Published 2015“…In the classification task, the presence of irrelevant features can significantly degrade the performance of classification algorithms,in terms of additional processing time, more complex models and the likelihood that the models have poor generalization power due to the over fitting problem.Practical applications of association rule mining often suffer from overwhelming number of rules that are generated, many of which are not interesting or not useful for the application in question.Removing rules comprised of irrelevant features can significantly improve the overall performance.In this paper, we explore and compare the use of a feature selection measure to filter out unnecessary and irrelevant features/attributes prior to association rules generation.The experiments are performed using a number of real-world datasets that represent diverse characteristics of data items.Empirical results confirm that by utilizing feature subset selection prior to association rule generation, a large number of rules with irrelevant features can be eliminated.More importantly, the results reveal that removing rules that hold irrelevant features improve the accuracy rate and capability to retain the rule coverage rate of structural associative association.…”
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Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam
Published 2017“…At first, a peak classification algorithm is developed based on the general following processes including peak candidate identification, feature extraction, and classification. …”
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A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani
Published 2016“…The components of the AIRS2 algorithm that pose problems will be modified. This thesis proposes three new hybrid algorithms: The FRA-AIRS2 algorithm uses fuzzy logic to improve data reduction capability of AIRS2 and to solve the linearity problem associated with resource allocation of AIRS. …”
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Modified archive update mechanism of multi-objective particle swarm optimization in fuzzy classification and clustering
Published 2022“…Evolutionary algorithms have been extensively used to resolve problems associated with multiple and often conflicting objectives. …”
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An enhanced feature selection technique for classification of group-based holy quran verses
Published 2018“…This thesis is about proposing an enhanced feature selection technique for text classification applications. Text classification problem is primarily applied in document labeling. …”
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Integrated framework with association analysis for gene selection in microarray data classification
Published 2011“…The main challenge in building this classification system is the curse of dimensionality problem. …”
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Detection of corneal arcus using rubber sheet and machine learning methods
Published 2019“…The benchmark of the classification algorithm for CA is needed to analyze the optimal output of the algorithm. …”
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Thesis