• Shreerajsinh R Jadeja
• Prof. Satvik Khara
• Prof. Rikin Thakkar

Due to the increasing use of very large databases and data warehouses, mining useful information and helpful knowledge from transactions is evolving into an important research area. Thus, most of the classic algorithms proposed focused on batch mining, and did not utilize previously mined information in incrementally growing databases. In the past, researchers usually assumed databases were static to simplify data mining problems. This research presents a new scalable algorithm Delta+ for discovering closed frequent item sets. Exploits a divide-and-conquer approach. Adopts several optimizations aimed to save both space and time in computing item set closures and their supports. Propose a new effective and memory-efficient pruning technique. Algorithm is scalable and outperforms algorithms like FP-Growth, in some cases even better. The performance improvements become more and more significant as the support threshold is decreased.

Big Data; Incremental Mining; Frequent item-sets; Incremental data mining algorithm; Map-Reduce framework;

"A Survey on an effective incremental mining algorithm for frequent item-sets with big data based on MapReduce framework ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.1750-1756, July-2018, Available :http://www.jetir.org/papers/JETIRC006303.pdf

"A Survey on an effective incremental mining algorithm for frequent item-sets with big data based on MapReduce framework ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp1750-1756, July-2018, Available at : http://www.jetir.org/papers/JETIRC006303.pdf