• Pinky Sen
• Shubham Gangrade
• Manish Rai

Data Mining is an analytic process designed for the search of exact patterns and/or proper relationship between variables to scrutinize data and then to approve the exploration with the help of detected patterns and to new subsets of data. Frequent pattern mining is most easily explained by introducing market basket analysis (or affinity analysis), characteristic practice for which it is familiar. In this paper, an efficient hybrid algorithm was designed using a unifying process of the algorithms Improved Apriori and SPMF ECLAT. Results indicate that the proposed algorithm faster execution time and generate frequent rules with different-different support count.

Data Mining, Hybrid Algorithm, Frequent Iemset, Improved Apriori, SPMF Eclat.

"Improved Apriori with Sequential Pattern Mining Framework for Frequent Itemset Mining in Data Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.632-636, April-2019, Available :http://www.jetir.org/papers/JETIR1904292.pdf

"Improved Apriori with Sequential Pattern Mining Framework for Frequent Itemset Mining in Data Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp632-636, April-2019, Available at : http://www.jetir.org/papers/JETIR1904292.pdf