• Vikram Rajpoot
• Shanu kumar
• Bharat Mishra

Data mining is the way towards separating valuable information from this overflowed data, which helps in settling on productive future decisions in these fields. Frequent item-set mining is an important step in finding association rules. Association rule mining (ARM) is the imperative piece of data mining, which helps to predict the association among various data items. In this paper, we apply a modified éclat algo to design an effective model. The emphasis was on the execution time modified éclat use a unifying process and produce preferable outcomes than both improved Apriori & FP-Growth. It reduces the scanning time. It is faster than existing approach.

Data Mining, , ARM algorithms, Association rule mining: Apriori, FP-Growth, Eclat.

"An Enhanced Eclat Algorithm for Mining Association Rules using data mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 9, page no.695-700, September-2018, Available :http://www.jetir.org/papers/JETIR1809744.pdf

"An Enhanced Eclat Algorithm for Mining Association Rules using data mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 9, page no. pp695-700, September-2018, Available at : http://www.jetir.org/papers/JETIR1809744.pdf