<?xml version="1.0" encoding="UTF-8"?><!-- generator="wordpress.com" -->
<rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	>

<channel>
	<title>hadoop-and-map-reduce &amp;laquo; WordPress.com Tag Feed</title>
	<link>http://en.wordpress.com/tag/hadoop-and-map-reduce/</link>
	<description>Feed of posts on WordPress.com tagged "hadoop-and-map-reduce"</description>
	<pubDate>Sun, 26 May 2013 02:58:28 +0000</pubDate>

	<generator>http://en.wordpress.com/tags/</generator>
	<language>en</language>

<item>
<title><![CDATA[Analytic is your Doctor's Friend]]></title>
<link>http://pkghosh.wordpress.com/2013/03/18/analytic-is-your-doctors-friend/</link>
<pubDate>Tue, 19 Mar 2013 06:45:49 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2013/03/18/analytic-is-your-doctors-friend/</guid>
<description><![CDATA[In this post, I will be venturing into the medical domain and show how big data analytic can play a]]></description>
<content:encoded><![CDATA[In this post, I will be venturing into the medical domain and show how big data analytic can play a]]></content:encoded>
</item>
<item>
<title><![CDATA[Stop the Customer Separation Pain with Bayesian Classifier]]></title>
<link>http://pkghosh.wordpress.com/2013/02/19/stop-the-customer-separation-pain-bayesian-classifier/</link>
<pubDate>Wed, 20 Feb 2013 07:01:18 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2013/02/19/stop-the-customer-separation-pain-bayesian-classifier/</guid>
<description><![CDATA[In my last post, we did some exploratory analytic for customer churn. We identified the parameters t]]></description>
<content:encoded><![CDATA[In my last post, we did some exploratory analytic for customer churn. We identified the parameters t]]></content:encoded>
</item>
<item>
<title><![CDATA[Explore Customer Churn with Cramer Index]]></title>
<link>http://pkghosh.wordpress.com/2013/01/31/explore-with-cramer-index/</link>
<pubDate>Fri, 01 Feb 2013 04:52:08 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2013/01/31/explore-with-cramer-index/</guid>
<description><![CDATA[Classification problems involve predicting a response variable based on  a set of feature variables]]></description>
<content:encoded><![CDATA[Classification problems involve predicting a response variable based on  a set of feature variables]]></content:encoded>
</item>
<item>
<title><![CDATA[Get Social with Pearson Correlation]]></title>
<link>http://pkghosh.wordpress.com/2012/12/31/get-social-with-pearson-correlation/</link>
<pubDate>Tue, 01 Jan 2013 02:04:01 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2012/12/31/get-social-with-pearson-correlation/</guid>
<description><![CDATA[In one of my earlier posts, I discussed about using Pearson correlation for making social recommenda]]></description>
<content:encoded><![CDATA[In one of my earlier posts, I discussed about using Pearson correlation for making social recommenda]]></content:encoded>
</item>
<item>
<title><![CDATA[Semantic Matching with Hadoop]]></title>
<link>http://pkghosh.wordpress.com/2012/11/16/semantic-matching-with-hadoop/</link>
<pubDate>Fri, 16 Nov 2012 11:13:45 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2012/11/16/semantic-matching-with-hadoop/</guid>
<description><![CDATA[Recently, I had a request to support semantic matching in sifarish, my open source matching and reco]]></description>
<content:encoded><![CDATA[Recently, I had a request to support semantic matching in sifarish, my open source matching and reco]]></content:encoded>
</item>
<item>
<title><![CDATA[Relative Density and Outliers]]></title>
<link>http://pkghosh.wordpress.com/2012/10/18/relative-density-and-outliers/</link>
<pubDate>Fri, 19 Oct 2012 06:32:20 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2012/10/18/relative-density-and-outliers/</guid>
<description><![CDATA[Recently I did some work on my open source fraud analytic project beymani. I implemented one of the]]></description>
<content:encoded><![CDATA[Recently I did some work on my open source fraud analytic project beymani. I implemented one of the]]></content:encoded>
</item>
<item>
<title><![CDATA[From Item Correlation to Rating Prediction]]></title>
<link>http://pkghosh.wordpress.com/2012/09/03/from-item-correlation-to-rating-prediction/</link>
<pubDate>Tue, 04 Sep 2012 05:01:30 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2012/09/03/from-item-correlation-to-rating-prediction/</guid>
<description><![CDATA[The ultimate goal of any recommendation engine is predict rating for items that an user has not enga]]></description>
<content:encoded><![CDATA[The ultimate goal of any recommendation engine is predict rating for items that an user has not enga]]></content:encoded>
</item>
<item>
<title><![CDATA[Big Web Checkout Abandonment]]></title>
<link>http://pkghosh.wordpress.com/2012/08/10/big-web-checkout-abandonment/</link>
<pubDate>Sat, 11 Aug 2012 06:37:15 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2012/08/10/big-web-checkout-abandonment/</guid>
<description><![CDATA[The topic for this post, is of interest to any online retailer. Shopping cart abandonment is dreaded]]></description>
<content:encoded><![CDATA[The topic for this post, is of interest to any online retailer. Shopping cart abandonment is dreaded]]></content:encoded>
</item>
<item>
<title><![CDATA[It's a lonely life for outliers ]]></title>
<link>http://pkghosh.wordpress.com/2012/06/18/its-a-lonely-life-for-outliers/</link>
<pubDate>Tue, 19 Jun 2012 05:54:19 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2012/06/18/its-a-lonely-life-for-outliers/</guid>
<description><![CDATA[In this post, I am back to outliers and fraud analytic. In this earlier post, I did an overview of o]]></description>
<content:encoded><![CDATA[In this post, I am back to outliers and fraud analytic. In this earlier post, I did an overview of o]]></content:encoded>
</item>
<item>
<title><![CDATA[Big Web Analytic]]></title>
<link>http://pkghosh.wordpress.com/2012/06/05/big-web-analytic/</link>
<pubDate>Wed, 06 Jun 2012 06:29:01 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2012/06/05/big-web-analytic/</guid>
<description><![CDATA[I had started on a Hadoop based web analytic open source project some time ago. Recently I did some]]></description>
<content:encoded><![CDATA[I had started on a Hadoop based web analytic open source project some time ago. Recently I did some]]></content:encoded>
</item>
<item>
<title><![CDATA[Hive Plays Well with JSON]]></title>
<link>http://pkghosh.wordpress.com/2012/05/06/hive-plays-well-with-json/</link>
<pubDate>Sun, 06 May 2012 07:17:27 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2012/05/06/hive-plays-well-with-json/</guid>
<description><![CDATA[Hive is an abstraction on Hadoop Map Reduce. It provides a SQL like interface for querying HDFS data]]></description>
<content:encoded><![CDATA[Hive is an abstraction on Hadoop Map Reduce. It provides a SQL like interface for querying HDFS data]]></content:encoded>
</item>
<item>
<title><![CDATA[Socially Accepted Recommendation]]></title>
<link>http://pkghosh.wordpress.com/2012/04/21/socially-accepted-recommendation/</link>
<pubDate>Sun, 22 Apr 2012 06:29:02 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2012/04/21/socially-accepted-recommendation/</guid>
<description><![CDATA[All my earlier posts on recommendation  systems focused on the so called content based recommendatio]]></description>
<content:encoded><![CDATA[All my earlier posts on recommendation  systems focused on the so called content based recommendatio]]></content:encoded>
</item>
<item>
<title><![CDATA[Fraudsters are not Model Citizens]]></title>
<link>http://pkghosh.wordpress.com/2012/02/18/fraudsters-are-not-model-citizens/</link>
<pubDate>Sat, 18 Feb 2012 08:55:35 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2012/02/18/fraudsters-are-not-model-citizens/</guid>
<description><![CDATA[In my earlier post, I did an overview of the outlier detection techniques in big data and specifical]]></description>
<content:encoded><![CDATA[In my earlier post, I did an overview of the outlier detection techniques in big data and specifical]]></content:encoded>
</item>
<item>
<title><![CDATA[Warm Starting a Recommender with Hadoop]]></title>
<link>http://pkghosh.wordpress.com/2012/01/09/warm-starting-recommender-with-hadoop/</link>
<pubDate>Tue, 10 Jan 2012 06:29:19 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2012/01/09/warm-starting-recommender-with-hadoop/</guid>
<description><![CDATA[In my earlier post I discussed the solution for cold starting a recommender. Cold starting refers to]]></description>
<content:encoded><![CDATA[In my earlier post I discussed the solution for cold starting a recommender. Cold starting refers to]]></content:encoded>
</item>
<item>
<title><![CDATA[Similarity Based Recommendation - Tossed up with Text Analytic   ]]></title>
<link>http://pkghosh.wordpress.com/2011/12/15/similarity-based-recommendation-text-analytic/</link>
<pubDate>Fri, 16 Dec 2011 07:34:33 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2011/12/15/similarity-based-recommendation-text-analytic/</guid>
<description><![CDATA[In my last post I mentioned that similarity based recommendation engine in sifarish only considered]]></description>
<content:encoded><![CDATA[In my last post I mentioned that similarity based recommendation engine in sifarish only considered]]></content:encoded>
</item>
<item>
<title><![CDATA[Similarity Based Recommendation - Hadoop Way]]></title>
<link>http://pkghosh.wordpress.com/2011/11/28/similarity-based-recommendation-hadoop-way/</link>
<pubDate>Tue, 29 Nov 2011 07:19:53 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2011/11/28/similarity-based-recommendation-hadoop-way/</guid>
<description><![CDATA[In my earlier post, I discussed some of the basic  concepts for Similarity Based Recommendation. As]]></description>
<content:encoded><![CDATA[In my earlier post, I discussed some of the basic  concepts for Similarity Based Recommendation. As]]></content:encoded>
</item>
<item>
<title><![CDATA[Multi Cluster Hadoop Job Monitoring]]></title>
<link>http://pkghosh.wordpress.com/2011/07/30/multi-cluster-hadoop-job-monitoring/</link>
<pubDate>Sun, 31 Jul 2011 04:09:43 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2011/07/30/multi-cluster-hadoop-job-monitoring/</guid>
<description><![CDATA[I spend lot of time tracking and monitoring Hadoop jobs running across multiple clusters in my curre]]></description>
<content:encoded><![CDATA[I spend lot of time tracking and monitoring Hadoop jobs running across multiple clusters in my curre]]></content:encoded>
</item>
<item>
<title><![CDATA[Visitor Conversion with Bayesian Discriminant and Hadoop ]]></title>
<link>http://pkghosh.wordpress.com/2011/06/29/visitor-conversion-with-bayesian-discriminant-and-hadoop/</link>
<pubDate>Wed, 29 Jun 2011 07:37:45 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2011/06/29/visitor-conversion-with-bayesian-discriminant-and-hadoop/</guid>
<description><![CDATA[You have lots of visitors on your eCommerce web site and obviously you would like most of them to co]]></description>
<content:encoded><![CDATA[You have lots of visitors on your eCommerce web site and obviously you would like most of them to co]]></content:encoded>
</item>
<item>
<title><![CDATA[Hadoop Orchestration]]></title>
<link>http://pkghosh.wordpress.com/2011/05/22/hadoop-orchestration/</link>
<pubDate>Mon, 23 May 2011 05:53:22 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2011/05/22/hadoop-orchestration/</guid>
<description><![CDATA[Most data processing tasks with Hadoop require multiple Hadoop jobs with dependencies between them.]]></description>
<content:encoded><![CDATA[Most data processing tasks with Hadoop require multiple Hadoop jobs with dependencies between them.]]></content:encoded>
</item>
<item>
<title><![CDATA[Map Reduce Secondary Sort Does It All]]></title>
<link>http://pkghosh.wordpress.com/2011/04/13/map-reduce-secondary-sort-does-it-all/</link>
<pubDate>Wed, 13 Apr 2011 07:09:09 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2011/04/13/map-reduce-secondary-sort-does-it-all/</guid>
<description><![CDATA[I came across a question in Stack Overflow recently related to calculating a web chat room statistic]]></description>
<content:encoded><![CDATA[I came across a question in Stack Overflow recently related to calculating a web chat room statistic]]></content:encoded>
</item>
<item>
<title><![CDATA[Recommendation Engine Powered by Hadoop (Part 2)]]></title>
<link>http://pkghosh.wordpress.com/2010/10/31/recommendation-engine-powered-by-hadoop-part-2/</link>
<pubDate>Mon, 01 Nov 2010 01:10:45 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2010/10/31/recommendation-engine-powered-by-hadoop-part-2/</guid>
<description><![CDATA[In Part 1 of this post the focus was on finding the correlation between items, based on rating data]]></description>
<content:encoded><![CDATA[In Part 1 of this post the focus was on finding the correlation between items, based on rating data]]></content:encoded>
</item>
<item>
<title><![CDATA[Recommendation Engine Powered by Hadoop (Part 1)]]></title>
<link>http://pkghosh.wordpress.com/2010/10/19/recommendation-engine-powered-by-hadoop-part-1/</link>
<pubDate>Tue, 19 Oct 2010 07:44:00 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2010/10/19/recommendation-engine-powered-by-hadoop-part-1/</guid>
<description><![CDATA[Personalized recommendations are ubiquitous in social network and shopping sites these days. How do]]></description>
<content:encoded><![CDATA[Personalized recommendations are ubiquitous in social network and shopping sites these days. How do]]></content:encoded>
</item>
<item>
<title><![CDATA[Log Analysis and Incident Reporting  with Hadoop]]></title>
<link>http://pkghosh.wordpress.com/2010/08/15/log-analysis-and-incident-reporting-with-hadoop/</link>
<pubDate>Mon, 16 Aug 2010 03:02:55 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2010/08/15/log-analysis-and-incident-reporting-with-hadoop/</guid>
<description><![CDATA[What do we have We have a very successful eCommerce site. We have lot of traffic  in our eCommerce s]]></description>
<content:encoded><![CDATA[What do we have We have a very successful eCommerce site. We have lot of traffic  in our eCommerce s]]></content:encoded>
</item>
<item>
<title><![CDATA[Folding, Cross Validation with Map Reduce]]></title>
<link>http://pkghosh.wordpress.com/2010/08/05/folding-cross-validation-with-map-reduce/</link>
<pubDate>Thu, 05 Aug 2010 07:12:18 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2010/08/05/folding-cross-validation-with-map-reduce/</guid>
<description><![CDATA[In my current web log data mining project using Hadoop, I am trying to build a predictive model for]]></description>
<content:encoded><![CDATA[In my current web log data mining project using Hadoop, I am trying to build a predictive model for]]></content:encoded>
</item>
<item>
<title><![CDATA[Cassandra and Hadoop]]></title>
<link>http://pkghosh.wordpress.com/2010/08/02/cassandra-hadoop/</link>
<pubDate>Mon, 02 Aug 2010 03:53:49 +0000</pubDate>
<dc:creator>Pranab</dc:creator>
<guid>http://pkghosh.wordpress.com/2010/08/02/cassandra-hadoop/</guid>
<description><![CDATA[I was always interested in mining patterns and knowledge from data. While working on a data mining p]]></description>
<content:encoded><![CDATA[I was always interested in mining patterns and knowledge from data. While working on a data mining p]]></content:encoded>
</item>

</channel>
</rss>
