Naba Jyoti Sarmah - Academia.edu (original) (raw)

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Papers by Naba Jyoti Sarmah

Intervals are found in many real life applications such as web uses; stock market information; pa... more Intervals are found in many real life applications such as web uses; stock market information; patient disease records; records maintained for occurrences of events, either man made or natural etc. Mining frequent intervals from such data allow us to group the transactions with similar behavior. Similar to frequent intervals, mining sparse intervals are also important. In this paper we define the notion of sparse and maximal sparse interval and also propose an algorithm for mining maximal sparse intervals. Computer programs were written and experimented on real life data set and results obtained have been reported. The correctness of the algorithm has also been proved.

Many real world data are closely associated with intervals. Mining frequent intervals from such d... more Many real world data are closely associated with intervals. Mining frequent intervals from such data allows us to group those data depending on some similarity. A few numbers of data mining approaches have been developed to discover frequent intervals from interval datasets. Here we present a complementary approach in which we search for sparse intervals in data. We present an efficient algorithm with a worst case time complexity of O(n log n) for mining maximal sparse intervals.

International Journal of Innovative Technology and Exploring Engineering, Aug 10, 2019

Interval data mining is used to extract unknown patterns, hidden rules, associations etc. associa... more Interval data mining is used to extract unknown patterns, hidden rules, associations etc. associated in interval based data. The extraction of closed interval is important because by mining the set of closed intervals and their support counts, the support counts of any interval can be computed easily. In this work an incremental algorithm for computing closed intervals together with their support counts from interval dataset is proposed. Many methods for mining closed intervals are available. Most of these methods assume a static data set as input and hence the algorithms are non-incremental. Real life data sets are however dynamic by nature. An efficient incremental algorithm called CI-Tree has been already proposed for computing closed intervals present in dynamic interval data. However this method could not compute the support values of the closed intervals. The proposed algorithm called SCI-Tree extracts all closed intervals together with their support values incrementally from the given interval data. Also, all the frequent closed intervals can be computed for any user defined minimum support with a single scan of SCI-Tree without revisiting the dataset. The proposed method has been tested with real life and synthetic datasets and results have been reported.

International Journal of Knowledge Engineering and Data Mining

International Journal of Computer Applications, 2014

Many real world data are closely associated with intervals. Mining frequent intervals from such d... more Many real world data are closely associated with intervals. Mining frequent intervals from such data allows us to group those data depending on some similarity. A few numbers of data mining approaches have been developed to discover frequent intervals from interval datasets. Here we present a complementary approach in which we search for sparse intervals in data. We present an efficient algorithm with a worst case time complexity of O(n log n) for mining maximal sparse intervals.

2014 IEEE International Advance Computing Conference (IACC), 2014

ABSTRACT In this paper we present an incremental algorithm for mining all the closed intervals fr... more ABSTRACT In this paper we present an incremental algorithm for mining all the closed intervals from interval dataset. Previous methods for mining closed intervals assume that the dataset is available at the starting of the process, whereas in practice, the data in the dataset may change over time. This paper describes an algorithm, which provides efficient method for mining closed intervals by using a data-structure called CI-Tree (Closed Interval Tree) in dynamically changing datasets. If a new interval is added in the dataset the algorithm modifies the CI-Tree without looking at the dataset. The proposed method is tested with various real life and synthetic datasets.

International Journal of Computer Applications, 2012

Intervals are found in many real life applications such as web uses; stock market information; pa... more Intervals are found in many real life applications such as web uses; stock market information; patient disease records; records maintained for occurrences of events, either man made or natural etc. Mining frequent intervals from such data allow us to group the transactions with similar behavior. Similar to frequent intervals, mining sparse intervals are also important. In this paper we define the notion of sparse and maximal sparse interval and also propose an algorithm for mining maximal sparse intervals. Computer programs were written and experimented on real life data set and results obtained have been reported. The correctness of the algorithm has also been proved.

Intervals are found in many real life applications such as web uses; stock market information; pa... more Intervals are found in many real life applications such as web uses; stock market information; patient disease records; records maintained for occurrences of events, either man made or natural etc. Mining frequent intervals from such data allow us to group the transactions with similar behavior. Similar to frequent intervals, mining sparse intervals are also important. In this paper we define the notion of sparse and maximal sparse interval and also propose an algorithm for mining maximal sparse intervals. Computer programs were written and experimented on real life data set and results obtained have been reported. The correctness of the algorithm has also been proved.

Many real world data are closely associated with intervals. Mining frequent intervals from such d... more Many real world data are closely associated with intervals. Mining frequent intervals from such data allows us to group those data depending on some similarity. A few numbers of data mining approaches have been developed to discover frequent intervals from interval datasets. Here we present a complementary approach in which we search for sparse intervals in data. We present an efficient algorithm with a worst case time complexity of O(n log n) for mining maximal sparse intervals.

International Journal of Innovative Technology and Exploring Engineering, Aug 10, 2019

Interval data mining is used to extract unknown patterns, hidden rules, associations etc. associa... more Interval data mining is used to extract unknown patterns, hidden rules, associations etc. associated in interval based data. The extraction of closed interval is important because by mining the set of closed intervals and their support counts, the support counts of any interval can be computed easily. In this work an incremental algorithm for computing closed intervals together with their support counts from interval dataset is proposed. Many methods for mining closed intervals are available. Most of these methods assume a static data set as input and hence the algorithms are non-incremental. Real life data sets are however dynamic by nature. An efficient incremental algorithm called CI-Tree has been already proposed for computing closed intervals present in dynamic interval data. However this method could not compute the support values of the closed intervals. The proposed algorithm called SCI-Tree extracts all closed intervals together with their support values incrementally from the given interval data. Also, all the frequent closed intervals can be computed for any user defined minimum support with a single scan of SCI-Tree without revisiting the dataset. The proposed method has been tested with real life and synthetic datasets and results have been reported.

International Journal of Knowledge Engineering and Data Mining

International Journal of Computer Applications, 2014

Many real world data are closely associated with intervals. Mining frequent intervals from such d... more Many real world data are closely associated with intervals. Mining frequent intervals from such data allows us to group those data depending on some similarity. A few numbers of data mining approaches have been developed to discover frequent intervals from interval datasets. Here we present a complementary approach in which we search for sparse intervals in data. We present an efficient algorithm with a worst case time complexity of O(n log n) for mining maximal sparse intervals.

2014 IEEE International Advance Computing Conference (IACC), 2014

ABSTRACT In this paper we present an incremental algorithm for mining all the closed intervals fr... more ABSTRACT In this paper we present an incremental algorithm for mining all the closed intervals from interval dataset. Previous methods for mining closed intervals assume that the dataset is available at the starting of the process, whereas in practice, the data in the dataset may change over time. This paper describes an algorithm, which provides efficient method for mining closed intervals by using a data-structure called CI-Tree (Closed Interval Tree) in dynamically changing datasets. If a new interval is added in the dataset the algorithm modifies the CI-Tree without looking at the dataset. The proposed method is tested with various real life and synthetic datasets.

International Journal of Computer Applications, 2012

Intervals are found in many real life applications such as web uses; stock market information; pa... more Intervals are found in many real life applications such as web uses; stock market information; patient disease records; records maintained for occurrences of events, either man made or natural etc. Mining frequent intervals from such data allow us to group the transactions with similar behavior. Similar to frequent intervals, mining sparse intervals are also important. In this paper we define the notion of sparse and maximal sparse interval and also propose an algorithm for mining maximal sparse intervals. Computer programs were written and experimented on real life data set and results obtained have been reported. The correctness of the algorithm has also been proved.