Design of Family Health Care Monitoring System Using Wireless Communication Technology

Abstract: Data mining is extensively studied field of research area, where most of the work is highlighted over knowledge discovery. Data stream is dynamic research area of data mining. A data stream is an enormous sequence of data elements continuously generated at a fast rate. In data streams, huge amount of data continuously inserted and queried, such data has very large database. The data stream is motivated by emerging applications involving massive data sets for example, consumer click streams and telephone records, bulky sets of web pages, multimedia datas, and financial transactions and so on. It raises new problems for the data stream community in terms of how to mine continuous arrival of high speed data items. Recently many researchers have focused on mining data streams and they proposed many techniques for data stream classification, data stream clustering and finding frequent items from data streams. Data stream clustering techniques are highly helpful to cluster the similar data items in data streams and also to detect the outliers, so they are called cluster based outlier detection. This main objective of this research work is to perform the clustering process in data streams and detecting the outliers in data streams. In this research work, two clustering algorithms namely BIRCH with K-Means and Birch with CLARANS are used for clustering the data items and finding the outliers in data streams. Different types, sizes of data sets and two performance factors such as clustering accuracy and outlier detection accuracy are used for analysis. By analysing the experimental results, it is observed that the proposed BIRCH with CLARANS clustering algorithm performance is more accurate than the existing algorithm BIRCH with K-Means.

Keywords: Data stream, Data stream Clustering, Outlier detection, Data mining