Bhargavi Peddireddy* | Anuradha Ch | Sri R. Chandra Murty Patnala1
OPEN ACCESS
Data mining is a process of finding undisclosed, unknown and interested patterns from databases. It has led to the many techniques, and they emphasis on mining frequent patterns to capture patterns whose occurrence is frequent, unusual and rare occurrence patterns. The research has led to optimize the performance of the techniques with its applications. But, traditional data mining techniques are limited to the databases which exhibits static behavior. But, the real time applications like sensors and stock data exhibits the behavior where the incoming data speed is fast and the cumulated data is huge. Such kind of databases are named as Data streams. The compatibility of data streams with the applications has led to the many issues and challenges. It has been motivated researchers to propose various frameworks and algorithms to speed up the mining process. In this paper, we discuss the various Applications of Data Streams, issues, and challenges. We discuss various models such are Landmark, Sliding window, Damped, and Title timed widow models. And also discuss various frequent itemset mining Algorithms for each models. In addition, this paper also discusses research issues and future direction towards for variety of pattern mining.
data streams,FPM, data base, itemset
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