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Papers by Nayanjyoti Mazumdar

Research paper thumbnail of Significance of Data Structures and Data Retrieval Techniques on Sequence Rule Mining Efficacy

International Journal on Recent and Innovation Trends in Computing and Communication, Oct 29, 2023

Sequence mining intends to discover rules from diverse datasets by implementing Rule Mining Algor... more Sequence mining intends to discover rules from diverse datasets by implementing Rule Mining Algorithms with efficient data structures and data retrieval techniques. Traditional algorithms struggle in handling variable support measures which may involve repeated reconstruction of the underlying data structures with changing thresholds. To address these issues the premiere Sequence Mining Algorithm, AprioriAll is implemented against an Educational and a Financial Dataset, using the HASH and the TRIE data structures with scan reduction techniques. Primary idea is to study the impact of data structures and retrieval techniques on the rule mining process in handling diverse datasets. Performance Evaluation Matrices- Support, Confidence and Lifts are considered for testing the efficacies of the algorithm in terms of memory requirements and execution time complexities. Results unveil the excellence of Hashing in tree construction time and memory overhead for fixed sets of pre-defined support thresholds. Whereas, TRIE may avoid reconstruction and is capable of handling dynamic support thresholds, leading to shorter rule discovery time but higher memory consumption. This study highlights the effectiveness of Hash and TRIE data structures considering the dataset characteristics during rule mining. It underscores the importance of appropriate data structures based on dataset features, scanning techniques, and user-defined parameters.

Research paper thumbnail of Correlation Analysis of Stock Index Data Features Using Sequential Rule Mining Algorithms

Algorithms for intelligent systems, Dec 31, 2022

Research paper thumbnail of Correlation Analysis of Stock Index Data Features Using Sequential Rule Mining Algorithms

Proceedings of International Conference on Data, Electronics and Computing, 2023

A massive changeover is witnessed in the stock markets worldwide during the recent pandemic situa... more A massive changeover is witnessed in the stock markets worldwide during the recent pandemic situations resulting in complicacy of people’s investment choices. Investors are anxious to minutely speculate the market movements for designing investment strategies and profit–loss analysis. For that, precise exploration of the historical market information is necessary to presume market fluctuations. The BSE Sensex and the NSE Nifty are the major capital market segments in India that manage a number of indices and are capable of representing the market trends. These indices may be discursively impacted by a number of components. The correlation and occurrence frequency of these components can unfold many unknown consequential information. In this study, we consider the Nifty 50 Index data of last 25 years and implemented the AprioriAll sequence mining algorithm using TRIE data structures. We take the features—previous days’ closing price, daily opening price, highest price, lowest price, closing price, shares traded, and the daily turnover to thoroughly investigated and analyse the correlation among them and verify their impacts on the overall market movements. A comparison of the in-memory space requirements for holding the generated candidate sequences while implementing the algorithm is also presented.

Research paper thumbnail of Significance of Data Structures and Data Retrieval Techniques on Sequence Rule Mining Efficacy

Auricle Global Society of Education and Research, 2023

Sequence mining intends to discover rules from diverse datasets by implementing Rule Mining Algor... more Sequence mining intends to discover rules from diverse datasets by implementing Rule Mining Algorithms with efficient data structures and data retrieval techniques. Traditional algorithms struggle in handling variable support measures which may involve repeated reconstruction of the underlying data structures with changing thresholds. To address these issues the premiere Sequence Mining Algorithm, AprioriAll is implemented against an Educational and a Financial Dataset, using the HASH and the TRIE data structures with scan reduction techniques. Primary idea is to study the impact of data structures and retrieval techniques on the rule mining process in handling diverse datasets. Performance Evaluation Matrices- Support, Confidence and Lifts are considered for testing the efficacies of the algorithm in terms of memory requirements and execution time complexities. Results unveil the excellence of Hashing in tree construction time and memory overhead for fixed sets of pre-defined support thresholds. Whereas, TRIE may avoid reconstruction and is capable of handling dynamic support thresholds, leading to shorter rule discovery time but higher memory consumption. This study highlights the effectiveness of Hash and TRIE data structures considering the dataset characteristics during rule mining. It underscores the importance of appropriate data structures based on dataset features, scanning techniques, and user-defined parameters.

Research paper thumbnail of Significance of Data Structures and Data Retrieval Techniques on Sequence Rule Mining Efficacy

International Journal on Recent and Innovation Trends in Computing and Communication, Oct 29, 2023

Sequence mining intends to discover rules from diverse datasets by implementing Rule Mining Algor... more Sequence mining intends to discover rules from diverse datasets by implementing Rule Mining Algorithms with efficient data structures and data retrieval techniques. Traditional algorithms struggle in handling variable support measures which may involve repeated reconstruction of the underlying data structures with changing thresholds. To address these issues the premiere Sequence Mining Algorithm, AprioriAll is implemented against an Educational and a Financial Dataset, using the HASH and the TRIE data structures with scan reduction techniques. Primary idea is to study the impact of data structures and retrieval techniques on the rule mining process in handling diverse datasets. Performance Evaluation Matrices- Support, Confidence and Lifts are considered for testing the efficacies of the algorithm in terms of memory requirements and execution time complexities. Results unveil the excellence of Hashing in tree construction time and memory overhead for fixed sets of pre-defined support thresholds. Whereas, TRIE may avoid reconstruction and is capable of handling dynamic support thresholds, leading to shorter rule discovery time but higher memory consumption. This study highlights the effectiveness of Hash and TRIE data structures considering the dataset characteristics during rule mining. It underscores the importance of appropriate data structures based on dataset features, scanning techniques, and user-defined parameters.

Research paper thumbnail of Correlation Analysis of Stock Index Data Features Using Sequential Rule Mining Algorithms

Algorithms for intelligent systems, Dec 31, 2022

Research paper thumbnail of Correlation Analysis of Stock Index Data Features Using Sequential Rule Mining Algorithms

Proceedings of International Conference on Data, Electronics and Computing, 2023

A massive changeover is witnessed in the stock markets worldwide during the recent pandemic situa... more A massive changeover is witnessed in the stock markets worldwide during the recent pandemic situations resulting in complicacy of people’s investment choices. Investors are anxious to minutely speculate the market movements for designing investment strategies and profit–loss analysis. For that, precise exploration of the historical market information is necessary to presume market fluctuations. The BSE Sensex and the NSE Nifty are the major capital market segments in India that manage a number of indices and are capable of representing the market trends. These indices may be discursively impacted by a number of components. The correlation and occurrence frequency of these components can unfold many unknown consequential information. In this study, we consider the Nifty 50 Index data of last 25 years and implemented the AprioriAll sequence mining algorithm using TRIE data structures. We take the features—previous days’ closing price, daily opening price, highest price, lowest price, closing price, shares traded, and the daily turnover to thoroughly investigated and analyse the correlation among them and verify their impacts on the overall market movements. A comparison of the in-memory space requirements for holding the generated candidate sequences while implementing the algorithm is also presented.

Research paper thumbnail of Significance of Data Structures and Data Retrieval Techniques on Sequence Rule Mining Efficacy

Auricle Global Society of Education and Research, 2023

Sequence mining intends to discover rules from diverse datasets by implementing Rule Mining Algor... more Sequence mining intends to discover rules from diverse datasets by implementing Rule Mining Algorithms with efficient data structures and data retrieval techniques. Traditional algorithms struggle in handling variable support measures which may involve repeated reconstruction of the underlying data structures with changing thresholds. To address these issues the premiere Sequence Mining Algorithm, AprioriAll is implemented against an Educational and a Financial Dataset, using the HASH and the TRIE data structures with scan reduction techniques. Primary idea is to study the impact of data structures and retrieval techniques on the rule mining process in handling diverse datasets. Performance Evaluation Matrices- Support, Confidence and Lifts are considered for testing the efficacies of the algorithm in terms of memory requirements and execution time complexities. Results unveil the excellence of Hashing in tree construction time and memory overhead for fixed sets of pre-defined support thresholds. Whereas, TRIE may avoid reconstruction and is capable of handling dynamic support thresholds, leading to shorter rule discovery time but higher memory consumption. This study highlights the effectiveness of Hash and TRIE data structures considering the dataset characteristics during rule mining. It underscores the importance of appropriate data structures based on dataset features, scanning techniques, and user-defined parameters.