TY  - JOUR
TI  - Clustering of High Dimensional Data Streams by Implementing HPStream Method

AU  - C. Kondaiah
JO  - International Journal of Scientific Research in Computer Science, Engineering and Information Technology
PB  - Technoscience Academy
DA  - 2017/08/31
PY  - 2017
DO  - https://doi.org/10.32628/IJSRCSEIT
UR  - https://ijsrcseit.com/CSEIT1724138
VL  - 2
IS  - 4
SP  - 524	
EP  - 529
AB  - Clustering is an important task in mining evolving with data streams because of data streams produces the continuous and potentially unbounded sequential of data points [1].Such streams collecting the data from the different devices. However, naturally, streaming data is high-dimensional data [1]. High dimensional data streams are frequently very large and it may include outliers .Therefore such streaming data is an significance issue in data mining process. High-dimensional data is actually very difficult in classification, clustering and similarity search. Recently, DBSTREAM, single-scan, subspace methods are used for projected clusters over the high-dimensional data sets. These methods are difficult to generalize to high dimensional data streams because of the huge volume of data generated the automatically by simple transactions of day-to-day life. In this paper implemented a high-dimensional data streams clustering technique, known as HPStream. This technique consists of fade clustering structure and projected primarily based clustering. It is continuously updatable and it's accurate scalable on both the number of dimensions and quantity of the data streams, and it offers the better high-quality clusters as compare with the preceding records movement techniques.