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  <title>DSpace Collection:</title>
  <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/2730" />
  <subtitle />
  <id>http://localhost:8080/xmlui/handle/123456789/2730</id>
  <updated>2026-06-23T06:33:32Z</updated>
  <dc:date>2026-06-23T06:33:32Z</dc:date>
  <entry>
    <title>CLASSIFICATION METHODS FOR DATA STREAM MINING</title>
    <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/2731" />
    <author>
      <name>Jadhav, R.</name>
    </author>
    <author>
      <name>Sharma, N.</name>
    </author>
    <id>http://localhost:8080/xmlui/handle/123456789/2731</id>
    <updated>2020-02-14T07:32:25Z</updated>
    <published>2018-03-01T00:00:00Z</published>
    <summary type="text">Title: CLASSIFICATION METHODS FOR DATA STREAM MINING
Authors: Jadhav, R.; Sharma, N.
Abstract: Data Stream Mining is the process of extracting knowledge structures from continuous and rapid data records&#xD;
arriving at high speed. Stream mining is one of the emerging fields of research in Data Mining. With the growing use of&#xD;
Internet in this digital era, tremendous amount of data is generating exponentially which needs to be analysed. This data is&#xD;
continuous, very large in size and cannot be stored for a long time. So there is a need to processes the data as soon as it&#xD;
becomes available. Various algorithms are available for mining data from streams, which requires single or fewer number of&#xD;
scans. With the recent advancement in Internet of Things (IOT), huge data streams are generated, thus making stream&#xD;
mining one of the most promising area of research. This paper is a review of different Classification methods used for data&#xD;
stream mining.</summary>
    <dc:date>2018-03-01T00:00:00Z</dc:date>
  </entry>
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