Abstract
Current paper submits an altogether novel method to identify Frequent Itemset which applies approach of statistics further based on association rule mining which otherwise a commonly applied method in transactional database to trace high frequency itemset in data mining arena. The algorithm designs frequency distribution table, Algorithm applies the formulas of class intervals to design frequency distribution table. It calculates the frequency if item sets present in the database. It functions in two phases. Phase 1 is termed as filtering and debulking phase. Phase 2 is termed as application step alongside comparison step among class interval techniques. Threshold values are calculated within the algorithm to find frequent item sets. Threshold numeric is subjected on to the formed table showing frequency distribution and simultaneously products listed are also selected. It generates pairs by applying combination technique. Database is the alone requirement as input. Current paper therefore addresses the worth of identifying and searching frequent item sets in huge transactional database.
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Mishra, D., Sharma, S. (2021). Statistical Approach for Mining Frequent Item Sets. In: Luhach, A.K., Jat, D.S., Bin Ghazali, K.H., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2020. Communications in Computer and Information Science, vol 1393. Springer, Singapore. https://doi.org/10.1007/978-981-16-3660-8_61
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