Shravan Vishwanathan - Academia.edu (original) (raw)
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Graduate Center of the City University of New York
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Papers by Shravan Vishwanathan
There are different learning and classification algorithms that are used to learn patterns and ca... more There are different learning and classification algorithms that are used to learn patterns and categorize data according to the frequency and relationship between attributes in a given dataset. The desired result has always been higher accuracy in predicting future values or events from the given dataset. These algorithms are crucial in the field of knowledge discovery and data mining and have been extensively researched and improved for accuracy. In this paper we first review the literature survey of the common learning algorithms. Then, we analyse and compare common and popular learning algorithms like Neural Net and SVM. Classifying algorithms like Naive Bayes, BFT, Decision Stump have also been compared. The analysis has been made on the basis of precision and accuracy in prediction and classification of data. The comparison helps us know the performance of these algorithms under certain conditions.
IOSR Journal of Computer Engineering, 2014
Significant advancements have been made in the field of data mining and knowledge discovery. But ... more Significant advancements have been made in the field of data mining and knowledge discovery. But there is no universal algorithm that can be dynamic enough to extract information from a continuous data stream or from a large stored dataset. A combination of algorithms has to work cohesively in order for the system to yield accurate and timely results according to user specifications. Algorithms are expected to be fast, accurate and visually informative to the user. We have tried to analyse the most effective and recent algorithms and techniques which have been developed to mine information from different data sources. We project our insight on the working of these algorithms and elicit possible loopholes and limitations.
There are different learning and classification algorithms that are used to learn patterns and ca... more There are different learning and classification algorithms that are used to learn patterns and categorize data according to the frequency and relationship between attributes in a given dataset. The desired result has always been higher accuracy in predicting future values or events from the given dataset. These algorithms are crucial in the field of knowledge discovery and data mining and have been extensively researched and improved for accuracy. In this paper we first review the literature survey of the common learning algorithms. Then, we analyse and compare common and popular learning algorithms like Neural Net and SVM. Classifying algorithms like Naive Bayes, BFT, Decision Stump have also been compared. The analysis has been made on the basis of precision and accuracy in prediction and classification of data. The comparison helps us know the performance of these algorithms under certain conditions.
IOSR Journal of Computer Engineering, 2014
Significant advancements have been made in the field of data mining and knowledge discovery. But ... more Significant advancements have been made in the field of data mining and knowledge discovery. But there is no universal algorithm that can be dynamic enough to extract information from a continuous data stream or from a large stored dataset. A combination of algorithms has to work cohesively in order for the system to yield accurate and timely results according to user specifications. Algorithms are expected to be fast, accurate and visually informative to the user. We have tried to analyse the most effective and recent algorithms and techniques which have been developed to mine information from different data sources. We project our insight on the working of these algorithms and elicit possible loopholes and limitations.