Real-time Data Mining Algorithm Analysis and Application: A Review

The goal of real-time data mining is to find patterns and insights in massive volumes of data as they are created, rather than having to wait for data to be gathered and analyzed. Applications in government, business, and other fields where accurate and fast information is critical for reaction and decision-making need this technique. Rather of waiting for data to come or processing it in batches, algorithms and methods used in real-time data mining examine data in real-time. Problems arise when trying to account for the ever-changing nature, rapid pace, many data types, and large amounts of data involved in real-time data mining. In order to gain insights and enhance system performance, real-time data mining may analyze multi-dimensional data, including power grid data in the power business. Building intrusion detection systems (IDSs) based on real-time data mining allows for the processing of audit data and the real-time detection of intrusions, all while addressing concerns of usability, efficiency, and accuracy. Because of the critical nature of real-time data processing and the need for systems to adapt to dynamic situations, there is a pressing need for research into methods to guarantee the accuracy and dependability of these systems.