The Experts below are selected from a list of 42348 Experts worldwide ranked by ideXlab platform
Ye Ning - One of the best experts on this subject based on the ideXlab platform.
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Knowledge Discovery in Modern Information Data and its Advance
Journal of Nanjing Forestry University, 2003Co-Authors: Ye NingAbstract:Data Mining which discovers knowledge from massive data sets has been recognized by researchers and the industries.In this paper,several data Mining Patterns such as association Pattern,arrangement Pattern and serial Pattern are generalized and the characteristics of these data Mining Patterns are compared.Further,several data Mining algorithms such as decision trees,nerve network,genetic algorithm are introduced.Finally,a prospective of data Mining Pattern and data Mining algorithm are proposed.
K. K. V. V. S. Reddy - One of the best experts on this subject based on the ideXlab platform.
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Parallel String Matching with Linear Array, Butterfly and Divide and Conquer Models
Annals of Data Science, 2018Co-Authors: S. Viswanadha Raju, K. K. V. V. S. ReddyAbstract:String Matching is a technique of searching a Pattern in a text. It is the basic concept to extract the fruitful information from large volume of text, which is used in different applications like text processing, information retrieval, text Mining, Pattern recognition, DNA sequencing and data cleaning etc., . Though it is stated some of the simple mechanisms perform very well in practice, plenty of research has been published on the subject and research is still active in this area and there are ample opportunities to develop new techniques. For this purpose, this paper has proposed linear array based string matching, string matching with butterfly model and string matching with divide and conquer models for sequential and parallel environments. To assess the efficiency of the proposed models, the genome sequences of different sizes (10–100 Mb) are taken as input data set. The experimental results have shown that the proposed string matching algorithms performs very well compared to those of Brute force, KMP and Boyer moore string matching algorithms.
Chong Zhi-hong - One of the best experts on this subject based on the ideXlab platform.
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Mining Pattern from stream data based on counting
Computer Engineering, 2004Co-Authors: Chong Zhi-hongAbstract:Mining frequent items is a basic task in stream data Mining. Many approximation algorithms behave well in frequent items Mining,but can not control their memory consumption. 0-δ algorithm that can solve the memory consumption problem was proposed and implemented. Besides sufficient analysis in theory,some extensive tests were also presented to show this algorithm is effective and efficient.
S. Viswanadha raju - One of the best experts on this subject based on the ideXlab platform.
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N-Folded Parallel String Matching Mechanism
Annals of Data Science, 2016Co-Authors: Butchi Raju Katari, S. Viswanadha rajuAbstract:A massive requirement of information vitalized the importance of managing enormous amount of data. It becomes a herculean task to fetch the anticipated data from large data storage as it includes text processing, text Mining, Pattern recognition, data cleaning etc., The need for concurrent events and coming up with high performance processing models to extract data is a challenge to the researchers. One of the solutions to this challenge is concurrent process to match string on processing models. While, some of the mechanisms do perform very well in practice. Frequent works have been published on this subject and research is still active in this area as the scope and opportunities to develop the new techniques is perennial. This paper proposes N-folded parallel string matching mechanism. This mechanism would be able to divide the input sequence files into various parts and the same would be distributed to the processors. Considering this mechanism as a model, experiments have been conducted considering chloroplast, mitochondria and different categories of plants genome sequence file as input for different sizes with seven possible Patterns. The results of the experiment made evident that N-folded parallel string matching mechanism can reduce the processing time on a multi processor system.
S. Viswanadha Raju - One of the best experts on this subject based on the ideXlab platform.
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Parallel String Matching with Linear Array, Butterfly and Divide and Conquer Models
Annals of Data Science, 2018Co-Authors: S. Viswanadha Raju, K. K. V. V. S. ReddyAbstract:String Matching is a technique of searching a Pattern in a text. It is the basic concept to extract the fruitful information from large volume of text, which is used in different applications like text processing, information retrieval, text Mining, Pattern recognition, DNA sequencing and data cleaning etc., . Though it is stated some of the simple mechanisms perform very well in practice, plenty of research has been published on the subject and research is still active in this area and there are ample opportunities to develop new techniques. For this purpose, this paper has proposed linear array based string matching, string matching with butterfly model and string matching with divide and conquer models for sequential and parallel environments. To assess the efficiency of the proposed models, the genome sequences of different sizes (10–100 Mb) are taken as input data set. The experimental results have shown that the proposed string matching algorithms performs very well compared to those of Brute force, KMP and Boyer moore string matching algorithms.