The week's pick
Reseach Article
Incremental Missing Value Replacement Techniques for Stream Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 122 - Number 17 |
| Year of Publication: 2015 |
| Authors: Kinnari Patel, R G Mehta, M M Raghuvanshi, N N Vadnere |
10.5120/21791-5129
|
Kinnari Patel, R G Mehta, M M Raghuvanshi, N N Vadnere . Incremental Missing Value Replacement Techniques for Stream Data. International Journal of Computer Applications. 122, 17 ( July 2015), 9-13. DOI=10.5120/21791-5129
Abstract
Stream data mining is the process of excerpting knowledge structure from large, continuous data. For stream data, various techniques are proposed for preparing the data for data mining task. In recent years stream data have become a growing area for the researcher, but there are many issues occurring in classifying these data due to erroneous and noisy data. Change of trend in the data periodically produces major challenge for data miners. This research concentrates on incremental missing value replacement for stream data. The proposed method generates the value for the missing data considering the data type and data distribution. It also considers the concept drift in the data stream. The method is applied to different datasets and promising results derived.
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