• Amit Ahalawat
• Mr . Arun Kumar Jhapate

Distributed systems, which may be used to do calculations, are being created in response to the rapid development of resource sharing. Data mining, which has a wide variety of real-world applications, offers substantial tools for extracting meaningful and useable information from vast datasets. Traditional data mining approaches, on the other hand, assume that the data is collected centrally, kept in memory, and remains static. Managing and analyzing large volumes of data with limited resources is tough. Large amounts of data, for example, are quickly created and saved in several places. It gets more expensive to consolidate them in one spot. Furthermore, conventional data mining techniques often have a number of challenges and limits, including memory constraints, restricted computing power, and inadequate hard drive capacity, among others. To address the following challenges, distributed data mining has emerged as a viable solution in various applications. According to several authors, this study examines cutting-edge distributed data mining methods such as distributed common item-set mining, distributed frequent sequence mining, technical challenges with distributed systems, distributed clustering, and privacy-protected distributed data mining. Furthermore, each work is reviewed and compared to its peers.

DISTRIBUTED SYSTEMS, DATA MINING, PARALLEL PLATFORM, DISTRIBUTED CLUSTERING.

"Distributed Data Mining Methods & Strategies: An In-Depth Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 1, page no.c463-c477, January-2025, Available :http://www.jetir.org/papers/JETIR2501255.pdf

"Distributed Data Mining Methods & Strategies: An In-Depth Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 1, page no. ppc463-c477, January-2025, Available at : http://www.jetir.org/papers/JETIR2501255.pdf