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IJSTR >> Volume 9 - Issue 3, March 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Evaluation And A Reconcile Analysis Of Lda And Svm Category

[Full Text]



Seethamani P, Vinotha R



SVM classifier, Data mining, LDA, Sequence Classification, Rating Prediction.



Fixed sequence classification is an important task in data mining. The problem of sequence classification is the use of rules consisting of interesting items that are contained in the data sets labeled sequences and the included class labels. Interesting levels are calculated from a set of items that are in a class ordered by linking and maintaining each item. There are a number of security patterns that may be hidden in the database. A mining algorithm looks for a complete set of patterns and meets the minimum support threshold (frequency), highly efficient and scalable, with a number of database scans incorporating various types of user-specific constraints. In the existing system, sequence classification techniques use several machine learning algorithms such as NaiveBayes, neighboring K-neighbors, decision trees, hidden markov models, SVM. This exit methodology can handle cases of minor deviations in certain events and acknowledges the occurrence of the pattern several times. In this work, LDA classifiers are used to classify and train datasets to improve dataset accuracy and efficiency to facilitate user analysis. Machine learning process is implemented to maintain the effectiveness of the prediction methodology. LDA prediction compared to SVM to prove performance improvement and linear classification id is used to improve classifier accuracy. Finally, the quality of the miner pattern is evaluated by the proposed approach pattern as features in several classification features based on different features.



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