Mining High Utility Time Interval Sequences Using MapReduce Approach: Multiple Utility Framework
Mining high utility sequential patterns is observed to be a significant research in data mining. Several methods mine the sequential patterns while taking utility values into consideration. The patterns of this type can determine the order in which items were purchased, but not the time interval bet...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9961173/ |
| _version_ | 1828181194693410816 |
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| author | Sumalatha Saleti T. Jaya Lakshmi Mohd Wazih Ahmad |
| author_facet | Sumalatha Saleti T. Jaya Lakshmi Mohd Wazih Ahmad |
| author_sort | Sumalatha Saleti |
| collection | DOAJ |
| description | Mining high utility sequential patterns is observed to be a significant research in data mining. Several methods mine the sequential patterns while taking utility values into consideration. The patterns of this type can determine the order in which items were purchased, but not the time interval between them. The time interval among items is important for predicting the most useful real-world circumstances, including retail market basket data analysis, stock market fluctuations, DNA sequence analysis, and so on. There are a very few algorithms for mining sequential patterns those consider both the utility and time interval. However, they assume the same threshold for each item, maintaining the same unit profit. Moreover, with the rapid growth in data, the traditional algorithms cannot handle the big data and are not scalable. To handle this problem, we propose a distributed three phase MapReduce framework that considers multiple utilities and suitable for handling big data. The time constraints are pushed into the algorithm instead of pre-defined intervals. Also, the proposed upper bound minimizes the number of candidate patterns during the mining process. The approach has been tested and the experimental results show its efficiency in terms of run time, memory utilization, and scalability. |
| first_indexed | 2024-04-12T05:57:35Z |
| format | Article |
| id | doaj.art-ab135d866fd1402d8b43770f819608ba |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T05:57:35Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ab135d866fd1402d8b43770f819608ba2022-12-22T03:45:07ZengIEEEIEEE Access2169-35362022-01-011012330112331510.1109/ACCESS.2022.32242179961173Mining High Utility Time Interval Sequences Using MapReduce Approach: Multiple Utility FrameworkSumalatha Saleti0https://orcid.org/0000-0003-1368-4993T. Jaya Lakshmi1https://orcid.org/0000-0003-0183-4093Mohd Wazih Ahmad2https://orcid.org/0000-0001-5614-2591Department of Computer Science and Engineering, SRM University AP, Guntur, Amaravati, IndiaDepartment of Computer Science and Engineering, SRM University AP, Guntur, Amaravati, IndiaDepartment of Computer Science and Engineering, Adama Science and Technology University, Adama, EthiopiaMining high utility sequential patterns is observed to be a significant research in data mining. Several methods mine the sequential patterns while taking utility values into consideration. The patterns of this type can determine the order in which items were purchased, but not the time interval between them. The time interval among items is important for predicting the most useful real-world circumstances, including retail market basket data analysis, stock market fluctuations, DNA sequence analysis, and so on. There are a very few algorithms for mining sequential patterns those consider both the utility and time interval. However, they assume the same threshold for each item, maintaining the same unit profit. Moreover, with the rapid growth in data, the traditional algorithms cannot handle the big data and are not scalable. To handle this problem, we propose a distributed three phase MapReduce framework that considers multiple utilities and suitable for handling big data. The time constraints are pushed into the algorithm instead of pre-defined intervals. Also, the proposed upper bound minimizes the number of candidate patterns during the mining process. The approach has been tested and the experimental results show its efficiency in terms of run time, memory utilization, and scalability.https://ieeexplore.ieee.org/document/9961173/Data miningMapReduce frameworkmultiple utility thresholdssequential pattern miningtime interval patterns |
| spellingShingle | Sumalatha Saleti T. Jaya Lakshmi Mohd Wazih Ahmad Mining High Utility Time Interval Sequences Using MapReduce Approach: Multiple Utility Framework IEEE Access Data mining MapReduce framework multiple utility thresholds sequential pattern mining time interval patterns |
| title | Mining High Utility Time Interval Sequences Using MapReduce Approach: Multiple Utility Framework |
| title_full | Mining High Utility Time Interval Sequences Using MapReduce Approach: Multiple Utility Framework |
| title_fullStr | Mining High Utility Time Interval Sequences Using MapReduce Approach: Multiple Utility Framework |
| title_full_unstemmed | Mining High Utility Time Interval Sequences Using MapReduce Approach: Multiple Utility Framework |
| title_short | Mining High Utility Time Interval Sequences Using MapReduce Approach: Multiple Utility Framework |
| title_sort | mining high utility time interval sequences using mapreduce approach multiple utility framework |
| topic | Data mining MapReduce framework multiple utility thresholds sequential pattern mining time interval patterns |
| url | https://ieeexplore.ieee.org/document/9961173/ |
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