• Naik-Nimbalkar S.D.
• Patil B.M.

Mining massive and high-speed data streams among the main contemporary challenges in now days. This calls for methods displaying a high computational efficacy, with ability to continuously update their structure and handle ever-arriving big number of instances. In this work, we present a new distributed classifier based on the popular nearest neighbor concept. This method includes a technique to perform a search operation with the help of keyword. Additionally, we propose an efficient incremental instance selection method for massive data streams that continuously update and remove outdated examples from the case-base. This alleviates the high computational requirements of the original classifier, thus making it suitable for the considered problem. Experimental study conducted on a set of real-life massive data streams proves the usefulness of the proposed solution and shows that we are able to provide the first efficient nearest neighbor solution for high-speed big and streaming data.

Nearest Neighbor, Data streams, Keyword Search

"Nearest Neighbor Set Search With Keyword", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.477-480, June 2019, Available :http://www.jetir.org/papers/JETIR1906W62.pdf

"Nearest Neighbor Set Search With Keyword", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp477-480, June 2019, Available at : http://www.jetir.org/papers/JETIR1906W62.pdf