Search Results - (( based applications mining algorithm ) OR ( _ applications usage algorithm ))*

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    A web usage mining approach based on LCS algorithm in online predicting recommendation systems by Jalali, Mehrdad, Mustapha, Norwati, Sulaiman, Md. Nasir, Mamat, Ali

    Published 2008
    “…To provide online prediction efficiently, we advance an architecture for online predicting in Web Usage Mining system and propose a novel approach based on LCS algorithm for classifying user navigation patterns for predicting users' future requests. …”
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    Article
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    Web Usage Mining for UUM Learning Care Using Association Rules by Azizul Azhar, Ramli

    Published 2004
    “…In order to produce the university E-Learning (UUM Educare) portal usage patterns and user behaviors, this paper implements the high level process of Web usage mining using basic Association Rules algorithm - Apriori Algorithm. …”
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    Thesis
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    Web usage mining for UUM learning care using association rules by Ramli, Azizul Azhar

    Published 2004
    “…In order to produce the university E-Learning (UUM Educare) portal usage patterns and user behaviors, this paper implements the high level process of Web usage mining using basic Association Rules algorithm - Apriori Algorithm. …”
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    Thesis
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    Mining Web usage using FRS by Omar, Rosli, Abdullah, Zainatul Shima

    Published 2018
    “…Web Usage Mining (WUM) is the application of data mining methods in extracting potentially useful information from web usage data. …”
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    Proceeding Paper
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    Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

    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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    Evaluation and optimization of frequent, closed and maximal association rule based classification by Mohd Shaharanee, Izwan Nizal, Hadzic, Fedja

    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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    Article
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    ANALYSIS OF STOCK PRICE PREDICTION USING DATA MINING APPROACH by Mukhariz bin Muhamad, Mukhariz

    Published 2012
    “…Using Data Mining approach in training the algorithms that will produce the best results based on Public Listed Companies‟ stock price data that dates back until 1998. …”
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    Final Year Project
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    Profiling network traffic of Sultan Idris Shah building (BSIS) using data mining technique / Rusmawati Ishak by Ishak, Rusmawati

    Published 2018
    “…Based on the result, most higher traffic pass through the network that consume of bandwidth usage are DNS (UDP) with total of 74,819 hits, MySQL with total of 40,691 and HTTP service with 59,784 hits record. …”
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    Thesis
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    Development of intelligent hybrid learning system using clustering and knowledge-based neural networks for economic forecasting : First phase by Che Mat @ Mohd Shukor, Zamzarina, Md Sap, Mohd Noor

    Published 2004
    “…We proposed KMeans clustering algorithm that is based on multidimensional scaling, joined with neural knowledge based technique algorithm for supporting the learning module to generate interesting clusters that will generate interesting rules for extracting knowledge from stock exchange databases efficiently and accurately.…”
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    Conference or Workshop Item
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