We have over 12,000 students, from over 100 countries, within one of the safest campuses in the UK


93% of Lancaster students go into work or further study within six months of graduating

Home > Research > Publications & Outputs > Evolving local means method for clustering of s...
View graph of relations

« Back

Evolving local means method for clustering of streaming data

Research output: Contribution in Book/Report/ProceedingsConference contribution


Publication date06/2012
Host publicationFuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on
Number of pages8
ISBN (Electronic)978-1-4673-1505-0
ISBN (Print) 978-1-4673-1507-4
<mark>Original language</mark>English


A new on-line evolving clustering approach for streaming data is proposed in this paper. The approach is based on the concept that local mean of samples within a region has the highest density and the gradient of the density points towards the local mean. The algorithm merely requires recursive calculation of local mean and variance, due to which it easily meets the memory and time constraints for data stream processing. The experimental results using synthetic and benchmark datasets show that the proposed approach attains results at par with offline approach and is comparable to popular density-based mean-shift clustering yet it is significantly more efficient being one-pass and non-iterative.