LAUSR

Many different applications in the real world can generate huge amount of data, that has unconventional features including massive size, fast access, besides the evolving in its nature; this is data stream. Data stream clustering algorithms began to grow at breakneck speed. evolving Cauchy (eCauchy) is a significant algorithm of density-based data stream clustering. The major limitation of eCauchy is the high number of clusters generated in dynamic environments. This paper presents an evolving model for data stream by optimizing e-Cauchy algorithm to decrease the number of clusters and reach to an ideal number by implementing evolving mechanisms (adding, merging, splitting clusters) based on a specific membership function. Model is tested by two real datasets NSL-KDD99 and keystroke. Proposed model outperforms two other algorithms, e-Cauchy and FEAC-Stream. Model constructs five and four clusters with less time to implement 1.30 and 2.30 minutes respectively for each dataset.