Sireesha Rodda | Gitam University,Visakhapatnam,India (original) (raw)

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Papers by Sireesha Rodda

Advances in Intelligent Systems and Computing, 2015

Advances in Intelligent Systems and Computing, 2015

Association Rule Mining (ARM) with reference to fuzzy logic is used to further data mining tasks ... more Association Rule Mining (ARM) with reference to fuzzy logic is used to further data mining tasks for classification and clustering. Traditional Fuzzy ARM algorithms have failed to mine rules from high-dimensional data efficiently, since those are meant to deal with relatively much less number of attributes or dimensions. Fuzzy ARM with high-dimensional data is a challenging problem to be addressed. This paper uses a quick and economical Fuzzy ARM algorithm FAR-HD, which processes frequent item sets using a two-phased multiple-partition approach especially for large high-dimensional datasets. The proposed algorithm is an extension to the FAR-HD process in which it improves the accuracy in terms of associative soft category labels by building a framework for fuzzy associative classifier to leverage the functionality of fuzzy association rules. Fuzzy ARM represent latent and dominant patterns in the given dataset, such a classifier is anticipated to supply superb accuracy particularly in terms of fuzzy support.

International Journal of Engineering Science, Jan 1, 2011

Most of the classification techniques proposed to handle multi-class datasets address data that i... more Most of the classification techniques proposed to handle multi-class datasets address data that is roughly balanced in nature. However, in many real-world datasets, the classes have imbalanced data distribution, where some classes have few training examples when compared to other classes. Most of the classification techniques addressing imbalanced data consider only binary-class datasets. In this paper, we present our research in learning from multi-class imbalanced dataset without breaking it down into a series of binary class datasets. This paper also discusses about normalized strength score, a measure used for estimating the quality of the classification rules obtained.

International Journal of Engineering Science, Jan 1, 2011

Handling large set of class association rules is a challenging problem. In this paper, a new fram... more Handling large set of class association rules is a challenging problem. In this paper, a new framework has been proposed which uses class association rules generated from a complete set of frequent generators. The classification rules thus obtained are smaller in size, lesser in total number and non-redundant in nature when compared to those obtained from classification rules generated by a complete set of frequent itemsets. Furthermore, the scalability of the classifier is ensured by the use of trie datastructure to store the set of frequent generators which directly represent the class association rules in this case. Experiments and comparison against classifiers like C4.5and variants on imbalanced and balanced data confirm our claims.

Conference on Computational …, Jan 1, 2007

Associative classification system is more robust and makes predictions based on entire dataset. I... more Associative classification system is more robust and makes predictions based on entire dataset. In this paper, we propose some criteria for ranking the association rules. This improves the overall accuracy of the classifier. Our preliminary results with some UCI ML datasets are very encouraging.

iccima, Jan 1, 2007

Associative classification system is more robust and makes predictions based on entire dataset. I... more Associative classification system is more robust and makes predictions based on entire dataset. In this paper, we use rough sets for feature reduction. We have also introduced two new criteria for ranking the association rules. This improves the overall accuracy of the classifier. Our preliminary results with some UCI ML datasets are very encouraging.

Teaching Documents by Sireesha Rodda

Advances in Intelligent Systems and Computing, 2015

Advances in Intelligent Systems and Computing, 2015

Association Rule Mining (ARM) with reference to fuzzy logic is used to further data mining tasks ... more Association Rule Mining (ARM) with reference to fuzzy logic is used to further data mining tasks for classification and clustering. Traditional Fuzzy ARM algorithms have failed to mine rules from high-dimensional data efficiently, since those are meant to deal with relatively much less number of attributes or dimensions. Fuzzy ARM with high-dimensional data is a challenging problem to be addressed. This paper uses a quick and economical Fuzzy ARM algorithm FAR-HD, which processes frequent item sets using a two-phased multiple-partition approach especially for large high-dimensional datasets. The proposed algorithm is an extension to the FAR-HD process in which it improves the accuracy in terms of associative soft category labels by building a framework for fuzzy associative classifier to leverage the functionality of fuzzy association rules. Fuzzy ARM represent latent and dominant patterns in the given dataset, such a classifier is anticipated to supply superb accuracy particularly in terms of fuzzy support.

International Journal of Engineering Science, Jan 1, 2011

Most of the classification techniques proposed to handle multi-class datasets address data that i... more Most of the classification techniques proposed to handle multi-class datasets address data that is roughly balanced in nature. However, in many real-world datasets, the classes have imbalanced data distribution, where some classes have few training examples when compared to other classes. Most of the classification techniques addressing imbalanced data consider only binary-class datasets. In this paper, we present our research in learning from multi-class imbalanced dataset without breaking it down into a series of binary class datasets. This paper also discusses about normalized strength score, a measure used for estimating the quality of the classification rules obtained.

International Journal of Engineering Science, Jan 1, 2011

Handling large set of class association rules is a challenging problem. In this paper, a new fram... more Handling large set of class association rules is a challenging problem. In this paper, a new framework has been proposed which uses class association rules generated from a complete set of frequent generators. The classification rules thus obtained are smaller in size, lesser in total number and non-redundant in nature when compared to those obtained from classification rules generated by a complete set of frequent itemsets. Furthermore, the scalability of the classifier is ensured by the use of trie datastructure to store the set of frequent generators which directly represent the class association rules in this case. Experiments and comparison against classifiers like C4.5and variants on imbalanced and balanced data confirm our claims.

Conference on Computational …, Jan 1, 2007

Associative classification system is more robust and makes predictions based on entire dataset. I... more Associative classification system is more robust and makes predictions based on entire dataset. In this paper, we propose some criteria for ranking the association rules. This improves the overall accuracy of the classifier. Our preliminary results with some UCI ML datasets are very encouraging.

iccima, Jan 1, 2007

Associative classification system is more robust and makes predictions based on entire dataset. I... more Associative classification system is more robust and makes predictions based on entire dataset. In this paper, we use rough sets for feature reduction. We have also introduced two new criteria for ranking the association rules. This improves the overall accuracy of the classifier. Our preliminary results with some UCI ML datasets are very encouraging.