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	<title>Pure Binary &#187; Concepts in world of Database</title>
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		<title>Concept of Data Mining</title>
		<link>http://www.pure-binary.com/2008/10/19/concept-of-data-mining/</link>
		<comments>http://www.pure-binary.com/2008/10/19/concept-of-data-mining/#comments</comments>
		<pubDate>Sun, 19 Oct 2008 16:03:39 +0000</pubDate>
		<dc:creator>Deepak</dc:creator>
				<category><![CDATA[Concepts in world of Database]]></category>
		<category><![CDATA[data mining]]></category>
		<category><![CDATA[DBMS]]></category>
		<category><![CDATA[MANIT]]></category>
		<category><![CDATA[NIT bhopal]]></category>

		<guid isPermaLink="false">http://www.pure-binary.com/?p=100</guid>
		<description><![CDATA[Data Mining means extracting or mining the knowledge from the voluminous data. It is also sometimes referred as knowledge mining from databases, knowledge extraction, data or pattern analysis, data archaeology, and data dredging. Many people believe that Data Mining and Knowledge Data Discovery i.e. KDD are same. Knowledge Data Discovery and Data Mining are vast [...]]]></description>
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<p style="text-align: justify;"><span style="color: #ff6600;"><strong>Data Mining</strong></span> means extracting or mining the knowledge from the voluminous data. It is also sometimes referred as knowledge mining from databases, knowledge extraction, data or pattern analysis, data archaeology, and data dredging. Many people believe that <span style="color: #ff6600;"><strong>Data Mining </strong><span style="color: #000000;">and </span><strong>Knowledge Data Discovery </strong><span style="color: #000000;">i.e</span><strong>. KDD </strong><span style="color: #000000;">are same</span></span>. Knowledge Data Discovery and Data Mining are vast topic and also very interesting subject in Computer Science and Engineering here at <span style="color: #993300;"><strong><a href="http://www.manit.ac.in/" target="_blank">MANIT Bhopal</a></strong></span>. Working of search engines like Google also utilizes these concepts Data Mining. It can be a very good research topic especially for those who have good hold in <strong><span style="color: #ff6600;">DBMS</span></strong>. Knowledge Discovery involves sequence of processes. <span> </span><span> </span>They are as follows:</span></p>
<ul class="unIndentedList">
<li><span style="color: #ff6600;"><strong>Data Cleaning:</strong></span> This step involves removal of noisy and irrelevant data.</li>
<li><span style="color: #ff6600;"><strong>Data Integration</strong></span>: It involves the process of combining multiple data sources</li>
<li><span style="color: #ff6600;"><strong>Data Selection</strong>:</span><strong> </strong>In this step relevant data are retrieved from the Database so that they can be analyzed.</li>
<li><span style="color: #ff6600;"><strong>Data Transformation</strong>:</span> This is very crucial step in which the selected data for analysis is further used and transformed into desired and appropriate form. Particularly in multidimensional database the irrelevant dimension is removed.</li>
<li><span style="color: #ff6600;"><strong>Pattern Evaluation</strong>:</span> Here we identify to identify the truly interesting patterns representing knowledge based on some interestingness measures</li>
<li><span style="color: #ff6600;"><strong>Knowledge Presentation</strong>:</span> This is the final step in which the mined knowledge is represented to the end user in the form of bar graphs or spread sheets etc.</li>
</ul>
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<p style="text-align: justify;"><span> </span>Usually the first two steps are known as Data Preprocessing when combined. Though there are various other concepts which is needed to understand the complete process nicely such are Data Warehousing, OLAP Technology, various algorithms for analyzing the data etc. But the space will not suffice due to vastness of the topic. If you want any help in above concepts please leave the comment. Here at <a href="http://www.manit.ac.in/" target="_blank"><strong>NIT-Bhopal</strong></a> we are recommended the book of author named <span style="color: #ff6600;"><strong>Kamber</strong> </span>for this subject. The book is vast and interesting but requires good knowledge about the <span style="color: #ff6600;"><strong>DBMS</strong> <strong>concepts</strong>.</span></span></p>
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