Interaction and Scalability in Data Mining

Abstract

We are able to gather, store, and share vast amounts of data on a massive scale because to technological advancements in hardware and software. Data mining is the process of automatically identifying patterns in these massive amounts of data and extracting hidden knowledge. Research, marketing, financial analytics, and other application fields are just a few of the domains in which data mining technology finds usage outside of business intelligence. Finding designs in large record sets using computer is a process known as records mining. Extraction of information from a record collection and its subsequent transformation into a comprehensible framework for additional usage is the overall objective of the records mining technique. In actuality, information exploration is the analytical step of the KDD, or "understanding invention in data banks" technique. However, there are several computational obstacles in terms of processing time, memory, bandwidth, and power consumption when attempting to extract knowledge in the form of patterns from large data volumes. These difficulties have prompted the creation of distributed and parallel data analysis techniques as well as the use of cloud and grid computing. The purpose of knowledge discovery/data mining (KDD) and human-computer interaction (HCI) is to support human intelligence with machine intelligence. We shall talk about scalability and interaction in data mining in this paper.