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	<title>VP's Blog</title>
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	<link>http://venkateshprabhu.in/blog</link>
	<description>Fly High in Your Dreams</description>
	<pubDate>Mon, 22 Jun 2009 06:49:41 +0000</pubDate>
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			<item>
		<title>Data Mining Process Model: CRISP–DM (CRoss-Industry Standard Process)</title>
		<link>http://venkateshprabhu.in/blog/?p=78</link>
		<comments>http://venkateshprabhu.in/blog/?p=78#comments</comments>
		<pubDate>Wed, 17 Jun 2009 08:45:52 +0000</pubDate>
		<dc:creator>VP</dc:creator>
		
		<category><![CDATA[Data Mining]]></category>

		<category><![CDATA[Text Mining]]></category>

		<category><![CDATA[Modeling]]></category>

		<category><![CDATA[Project Management]]></category>

		<guid isPermaLink="false">http://venkateshprabhu.in/blog/?p=78</guid>
		<description><![CDATA[ 
There is a temptation in some companies, due to departmental inertia and compartmentalization, to approach data mining haphazardly, to reinvent the wheel and duplicate effort. A cross-industry standard was clearly required that is industry neutral, tool-neutral, and application-neutral. The Cross-Industry Standard Process for Data Mining (CRISP–DM) was developed in 1996 by analysts representing DaimlerChrysler, SPSS, [...]]]></description>
		<wfw:commentRss>http://venkateshprabhu.in/blog/?feed=rss2&amp;p=78</wfw:commentRss>
		</item>
		<item>
		<title>Data Mining Project Management</title>
		<link>http://venkateshprabhu.in/blog/?p=68</link>
		<comments>http://venkateshprabhu.in/blog/?p=68#comments</comments>
		<pubDate>Wed, 17 Jun 2009 07:59:53 +0000</pubDate>
		<dc:creator>VP</dc:creator>
		
		<category><![CDATA[Data Mining]]></category>

		<category><![CDATA[Text Mining]]></category>

		<category><![CDATA[Modeling]]></category>

		<category><![CDATA[Project Management]]></category>

		<guid isPermaLink="false">http://venkateshprabhu.in/blog/?p=68</guid>
		<description><![CDATA[


 
This article describes a series of issues that should be considered at the start of any data analysis or data mining project. It is important to define the problem in sufficient detail, in terms of both how the questions are to be answered and how the solutions will be delivered. On the basis of this [...]]]></description>
		<wfw:commentRss>http://venkateshprabhu.in/blog/?feed=rss2&amp;p=68</wfw:commentRss>
		</item>
		<item>
		<title>What is a “Model”?</title>
		<link>http://venkateshprabhu.in/blog/?p=66</link>
		<comments>http://venkateshprabhu.in/blog/?p=66#comments</comments>
		<pubDate>Wed, 17 Jun 2009 07:11:26 +0000</pubDate>
		<dc:creator>VP</dc:creator>
		
		<category><![CDATA[Data Mining]]></category>

		<category><![CDATA[Text Mining]]></category>

		<category><![CDATA[Modeling]]></category>

		<guid isPermaLink="false">http://venkateshprabhu.in/blog/?p=66</guid>
		<description><![CDATA[ 
It’s fine to say that a modeler builds a model, but what actually is a model? A model, in a general sense, is a replica of some other object that duplicates selected features of that larger object, but in a more convenient form. A plastic model World War II battleship, for instance, models the external [...]]]></description>
		<wfw:commentRss>http://venkateshprabhu.in/blog/?feed=rss2&amp;p=66</wfw:commentRss>
		</item>
		<item>
		<title>Modeling Data - Assumptions</title>
		<link>http://venkateshprabhu.in/blog/?p=59</link>
		<comments>http://venkateshprabhu.in/blog/?p=59#comments</comments>
		<pubDate>Wed, 17 Jun 2009 06:27:46 +0000</pubDate>
		<dc:creator>VP</dc:creator>
		
		<category><![CDATA[Data Mining]]></category>

		<category><![CDATA[Text Mining]]></category>

		<category><![CDATA[Modeling]]></category>

		<guid isPermaLink="false">http://venkateshprabhu.in/blog/?p=59</guid>
		<description><![CDATA[ 
Modeling of any data set is based on five key assumptions. They are worth reviewing since if any of them do not hold, no model will reflect the real world, except by luck! The Key assumptions are
 
1. Measurements of features of the world represent something real about the world.
2. Some persistent relationship exists between the [...]]]></description>
		<wfw:commentRss>http://venkateshprabhu.in/blog/?feed=rss2&amp;p=59</wfw:commentRss>
		</item>
		<item>
		<title>Text (Document) Information Retrieval Frameworks</title>
		<link>http://venkateshprabhu.in/blog/?p=52</link>
		<comments>http://venkateshprabhu.in/blog/?p=52#comments</comments>
		<pubDate>Sun, 17 May 2009 17:06:17 +0000</pubDate>
		<dc:creator>VP</dc:creator>
		
		<category><![CDATA[Text Mining]]></category>

		<category><![CDATA[Information Re]]></category>

		<category><![CDATA[Information Retrieval]]></category>

		<guid isPermaLink="false">http://venkateshprabhu.in/blog/?p=52</guid>
		<description><![CDATA[ 
Document retrieval is defined as the matching of some stated user query against a set of free-text records. These records could be any type of mainly unstructured text, such as newspaper articles, real estate records or paragraphs in a manual. User queries can range from multi-sentence full descriptions of an information need to a few [...]]]></description>
		<wfw:commentRss>http://venkateshprabhu.in/blog/?feed=rss2&amp;p=52</wfw:commentRss>
		</item>
		<item>
		<title>Opinion Mining - Reading The Minds of People</title>
		<link>http://venkateshprabhu.in/blog/?p=50</link>
		<comments>http://venkateshprabhu.in/blog/?p=50#comments</comments>
		<pubDate>Sun, 17 May 2009 17:02:28 +0000</pubDate>
		<dc:creator>VP</dc:creator>
		
		<category><![CDATA[My Research]]></category>

		<category><![CDATA[Text Mining]]></category>

		<category><![CDATA[Opinian Mining]]></category>

		<guid isPermaLink="false">http://venkateshprabhu.in/blog/?p=50</guid>
		<description><![CDATA[ 
Opinion mining (OM) is a recent discipline at the crossroads of information retrieval and computational linguistics which is concerned not with the topic a document is about, but with the opinion it expresses. An opinion is a private state that is not open to objective observation or verification. [Quirk et al., 1985]. Sentiment analysis, Sentiment [...]]]></description>
		<wfw:commentRss>http://venkateshprabhu.in/blog/?feed=rss2&amp;p=50</wfw:commentRss>
		</item>
		<item>
		<title>Seven ways to make a mark during a meeting</title>
		<link>http://venkateshprabhu.in/blog/?p=47</link>
		<comments>http://venkateshprabhu.in/blog/?p=47#comments</comments>
		<pubDate>Sun, 17 May 2009 16:57:31 +0000</pubDate>
		<dc:creator>VP</dc:creator>
		
		<category><![CDATA[General]]></category>

		<category><![CDATA[Personality Development]]></category>

		<guid isPermaLink="false">http://venkateshprabhu.in/blog/?p=47</guid>
		<description><![CDATA[ 
Trying to discover innovative methods to steal the show? Here&#8217;s an answer to all your questions as to how to stand out during the meetings at your workplace. Preparation and confidence are the two key factors that you would need to distinguish yourself in a meeting. If you are well prepared and have full confidence [...]]]></description>
		<wfw:commentRss>http://venkateshprabhu.in/blog/?feed=rss2&amp;p=47</wfw:commentRss>
		</item>
		<item>
		<title>10 Golden Rules for Building Models</title>
		<link>http://venkateshprabhu.in/blog/?p=44</link>
		<comments>http://venkateshprabhu.in/blog/?p=44#comments</comments>
		<pubDate>Sun, 17 May 2009 16:53:28 +0000</pubDate>
		<dc:creator>VP</dc:creator>
		
		<category><![CDATA[Data Mining]]></category>

		<category><![CDATA[Text Mining]]></category>

		<guid isPermaLink="false">http://venkateshprabhu.in/blog/?p=44</guid>
		<description><![CDATA[ 

Select clearly defined problems that will yield tangible benefits. 
Pecify the required solutions. 
Define how the solution delivered is going to be used. 
Understand as much as possible about the problem and the data set (the domain). 
Let the problem drive the modeling (i.e. tool selection, data preparation, etc). 
Stipulate assumptions. 
Refine the model iteratively. [...]]]></description>
		<wfw:commentRss>http://venkateshprabhu.in/blog/?feed=rss2&amp;p=44</wfw:commentRss>
		</item>
		<item>
		<title>The Document Categorization (Classification) Problem</title>
		<link>http://venkateshprabhu.in/blog/?p=40</link>
		<comments>http://venkateshprabhu.in/blog/?p=40#comments</comments>
		<pubDate>Sun, 17 May 2009 16:50:24 +0000</pubDate>
		<dc:creator>VP</dc:creator>
		
		<category><![CDATA[Text Mining]]></category>

		<category><![CDATA[Classification]]></category>

		<category><![CDATA[Clustering]]></category>

		<guid isPermaLink="false">http://venkateshprabhu.in/blog/?p=40</guid>
		<description><![CDATA[ 
The problem of Text categorization can be described as the classifications of text documents into multiple categories. We have a set of n categories {C1, C2, C3,….Cn} to which we assign m documents {D1, D2, Dm}.
 
The n categories are predefined with specific keywords that differentiate any category Ci from every other category Cj. The process [...]]]></description>
		<wfw:commentRss>http://venkateshprabhu.in/blog/?feed=rss2&amp;p=40</wfw:commentRss>
		</item>
		<item>
		<title>My Research Interests</title>
		<link>http://venkateshprabhu.in/blog/?p=29</link>
		<comments>http://venkateshprabhu.in/blog/?p=29#comments</comments>
		<pubDate>Sun, 17 May 2009 16:37:39 +0000</pubDate>
		<dc:creator>VP</dc:creator>
		
		<category><![CDATA[My Research]]></category>

		<guid isPermaLink="false">http://venkateshprabhu.in/blog/?p=29</guid>
		<description><![CDATA[ 
I have mentioned some of my research interests here.
 
Text Classification
 
Today&#8217;s organizations face a vast volume of knowledge and information. Most of the explicit knowledge is stored in different types of documents but only a few people (often only the authors of the documents) know where to locate them. A major approach for organizing information is [...]]]></description>
		<wfw:commentRss>http://venkateshprabhu.in/blog/?feed=rss2&amp;p=29</wfw:commentRss>
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