Sheeba sugantharani E. | Lady Doak College (original) (raw)

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Papers by Sheeba sugantharani E.

Dogo Rangsang Research Journal , UGC Care Group I Journal , 2022

Data mining involves identification of sequential patterns though huge amount of data. In data an... more Data mining involves identification of sequential patterns though huge amount of data. In data analysis process cluster classification analysis and machine intelligence were employed. Data mining helps in better analysis of medical bioinformatics. Classification is used for prediction outcomes and association is used to find rules affiliated with items having co-occurrence. The Weka software includes data pre-processing tools, classification / regression algorithms, clustering algorithms, association rule mining algorithms with attribute / subset evaluation methods for feature selection process. It support multiple platforms and it's written in java. Weka is used to define filters to transform the data in terms of Discretization, Normalization, Re-sampling, Attribute selection etc., Analysis of Bioinformatics gene expression has performed to predict the accuracy of frequent pattern mining algorithm in the diagnosis of hypothyroid.

Journal of Emerging Technologies and Innovative Research, 2019

Data mining involves identification of important trends or patterns through huge amounts of data.... more Data mining involves identification of important trends or patterns through huge amounts of data. Advanced statistical techniques such as cluster analysis, artificial intelligence and neural network techniques are used in the data analysis processes. Data mining helps in better analysis of geographical data, Genome and medical sector. Classification is used for predicting outcomes and association is used to find rules affiliated with items having co-occurrence. Frequent Itemset Mining (FIM) is an approach to discover association rules in datasets. Frequent Pattern Mining (FPM) is used for finding relationships among the items in a large database obtained from the cloud environment. Association rule mining is applied for obtaining the frequent patterns. Association rule mining and frequent itemset mining are two popular and widely studied data analysis techniques for a wide range of applications such as market basket analysis, healthcare, web usage mining, bioinformatics, personalized recommendation, network optimization, medical diagnosis. This paper reviews different frequent pattern mining algorithms with weighted, interesting pattern and uncertain databases. A brief comparison of various mining algorithms based on their metrics, dataset , inferences of their work with few drawbacks were summarized. According to the reviewed papers, it was observed that uncertain database requires larger storage space and it was a time consuming process. Moreover, various challenges include checking accuracy and efficiency with time bound, setting the threshold criteria, choosing the appropriate datastructure and number of transactions containing the itemset. IndexTerms-Frequent Pattern Mining, uncertain databases, Weighted frequent itemset mining, interesting patterns, BFIforest.

Dogo Rangsang Research Journal , UGC Care Group I Journal , 2022

Data mining involves identification of sequential patterns though huge amount of data. In data an... more Data mining involves identification of sequential patterns though huge amount of data. In data analysis process cluster classification analysis and machine intelligence were employed. Data mining helps in better analysis of medical bioinformatics. Classification is used for prediction outcomes and association is used to find rules affiliated with items having co-occurrence. The Weka software includes data pre-processing tools, classification / regression algorithms, clustering algorithms, association rule mining algorithms with attribute / subset evaluation methods for feature selection process. It support multiple platforms and it's written in java. Weka is used to define filters to transform the data in terms of Discretization, Normalization, Re-sampling, Attribute selection etc., Analysis of Bioinformatics gene expression has performed to predict the accuracy of frequent pattern mining algorithm in the diagnosis of hypothyroid.

Journal of Emerging Technologies and Innovative Research, 2019

Data mining involves identification of important trends or patterns through huge amounts of data.... more Data mining involves identification of important trends or patterns through huge amounts of data. Advanced statistical techniques such as cluster analysis, artificial intelligence and neural network techniques are used in the data analysis processes. Data mining helps in better analysis of geographical data, Genome and medical sector. Classification is used for predicting outcomes and association is used to find rules affiliated with items having co-occurrence. Frequent Itemset Mining (FIM) is an approach to discover association rules in datasets. Frequent Pattern Mining (FPM) is used for finding relationships among the items in a large database obtained from the cloud environment. Association rule mining is applied for obtaining the frequent patterns. Association rule mining and frequent itemset mining are two popular and widely studied data analysis techniques for a wide range of applications such as market basket analysis, healthcare, web usage mining, bioinformatics, personalized recommendation, network optimization, medical diagnosis. This paper reviews different frequent pattern mining algorithms with weighted, interesting pattern and uncertain databases. A brief comparison of various mining algorithms based on their metrics, dataset , inferences of their work with few drawbacks were summarized. According to the reviewed papers, it was observed that uncertain database requires larger storage space and it was a time consuming process. Moreover, various challenges include checking accuracy and efficiency with time bound, setting the threshold criteria, choosing the appropriate datastructure and number of transactions containing the itemset. IndexTerms-Frequent Pattern Mining, uncertain databases, Weighted frequent itemset mining, interesting patterns, BFIforest.