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A data mining approach to construct graduates employability model in Malaysia
Published 2011“…The performance of Bayes algorithms are also compared against a number of tree-based algorithms. …”
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Prime-based method for interactive mining of frequent patterns
Published 2010“…Thus far, a few efficient interactive mining algorithms have been proposed. …”
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A Data Mining Approach to Construct Graduates Employability Model in Malaysia
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Efficient prime-based method for interactive mining of frequent patterns.
Published 2011“…During the mining process, the mining algorithm reduces the number of candidate patterns and comparisons by using a new candidate set called candidate head set and several efficient pruning techniques. …”
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Frequent itemset mining using graph theory / Mohammad Arsyad Mohd Yakop
Published 2017“…There is a number of algorithms focusing on frequent itemsets mining (FIM) field, however, some of the problems still require attention, particularly when the mining process involves a high dimensional dataset. …”
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An extended ID3 decision tree algorithm for spatial data
Published 2011“…One of classification algorithms namely the ID3 algorithm which originally designed for a non-spatial dataset has been improved by other researchers in the previous work to construct a spatial decision tree from a spatial dataset containing polygon features only. …”
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A frequent pattern mining algorithm based on FP-growth without generating tree
Published 2010“…For a large database, constructing a large tree in the memory is a time consuming task and increase the time of execution. …”
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A frequent pattern mining algorithm based on FP-growth without generating tree
Published 2010“…It then divides the compressed database into a set of conditional databases (a special kind of projected database), each associated with one frequent item or pattern fragment, and mines each such database separately.For a large database, constructing a large tree in the memory is a time consuming task and increase the time of execution.In this paper we introduce an algorithm to generate frequent patterns without generating a tree and therefore improve the time complexity and memory complexity as well.Our algorithm works based on prime factorization, and is called Frequent Pattern- Prime Factorization (FPPF).…”
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Frequent Lexicographic Algorithm for Mining Association Rules
Published 2005“…The algorithm involved the construction of the nodes of a lexicographic tree that represent frequent patterns. …”
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Using unique-prime-factorization theorem to mine frequent patterns without generating tree
Published 2011“…However, for a large database, constructing a large tree in the memory is a time consuming task and increase the time of execution. …”
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A Scalable Algorithm for Constructing Frequent Pattern Tree
Published 2014“…The construction of FP-Tree is very important prior to frequent patterns mining. …”
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Modeling Teacher's Integrity Using Data Mining
Published 2010“…The dataset called teachers's integrity dataset contain 1500 records elaborates with 25 attributes including a target class were pre-processed and then mined using directed association rule mining algorithm namely Apriori. …”
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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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SPATIAL DATA MINING TOOLBOX FOR MAPPING SUITABILITY OF LANDFILL SITES USING NEURAL NETWORKS
Published 2016“…The multilayer perceptron (MLP) neural networks structure with a backpropagation learning algorithm is used. The dataset is mined from the north states in Malaysia. …”
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Enhanced Hopfield Neural Networks With Artificial Immune System Algorithm For Satisfiability Logic Programming
Published 2017“…The emergence of 3-Satisfiability (3-SAT) problem has produced a prolific number of works devoted to the field of logic and data mining. …”
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Mining Sequential Patterns using I-PrefixSpan
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Data Mining On Machine Breakdowns And Effectiveness Of Scheduled Maintenance
Published 2019“…Last but not least, some of the complex data mining tasks are not able to perform because of the limited algorithms and machine learning in Orange software.…”
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A new approach to present prototypes in clustering of time series
Published 2011“…There are considerable advances in clustering time series data in data mining concept. However, most of which use traditional approaches and try to customize the algorithms to be compatible with time series data. …”
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