Introduction data mining case studies

Data mining is used wherever there is digital data available today. Notable examples of data mining can be found throughout business, medicine, science, and surveillance. Privacy concerns and ethics[ edit ] While the term "data mining" itself may have no ethical implications, it is often associated with the mining of information in relation to peoples' behavior ethical and otherwise. A common way for this to occur is through data aggregation.

Introduction data mining case studies

About Razorfish Razorfisha digital advertising and marketing firm, segments users and customers based on the collection and analysis of non-personally identifiable data from browsing sessions. Doing so requires applying data mining methods across historical click streams to identify effective segmentation and categorization algorithms and techniques.

Algorithms are then implemented on systems that can batch execute at the appropriate scale against current data sets ranging in size from dozens of Gigabytes to Terabytes. Results of the analysis are loaded into ad-serving and cross-selling systems that in turn deliver the segmentation results in real time.

The Challenge A common issue Razorfish has found with customer segmentation is the need to process gigantic click stream data sets.

These large data sets are often the result of holiday shopping traffic on a retail website, or sudden dramatic growth on the data network of a media or social networking site.

Without the expensive computing resources, Razorfish risks losing clients that require Razorfish to have sufficient resources at hand during critical moments.

Account Options

As the sample data set grows i. Meanwhile, as the number of clients that utilize targeted advertising grows, access to on-demand compute and storage resources becomes a requirement. It was thus imperative for Razorfish to implement customer segmentation algorithms in a way that could be applied and executed independently of the scale of the incoming data and supporting infrastructure.

Prior to implementing Amazon Web Services AWSRazorfish relied on a traditional hosting environment that utilized high-cost SAN equipment for storage, a proprietary distributed log processing cluster of 30 servers, and several high-end SQL servers.

In preparation for the holiday season, demand for targeted advertising increased. Furthermore, due to downstream dependencies, they needed their daily processing cycle to complete within 18 hours.

However, given the increased data volume, Razorfish expected their processing cycle to extend past two days for each run even after the potential investment in human and computing resources.

See a Problem?

Why Amazon Web Services To deal with the combination of huge datasets and custom segmentation targeting activities, coupled with price sensitive clients, Razorfish decided to move away from their rigid data infrastructure status quo. This migration helped Razorfish process vast amounts of data to handle the need for rapid scaling at both the application and infrastructure levels.

Razorfish selected Ad Serving integration, AWS, Amazon Elastic MapReduce a hosted Apache Hadoop serviceCascading, and a variety of chosen applications to power their targeted advertising system based on these benefits: Elastic infrastructure from AWS allows capacity to be provisioned as needed based on load, reducing cost and the risk of processing delays.

Amazon Elastic MapReduce and Cascading lets Razorfish focus on application development without having to worry about time-consuming set-up, management, or tuning of Hadoop clusters or the compute capacity upon which they sit.

Amazon Elastic MapReduce with Cascading allows data processing in the cloud without any changes to the underlying algorithms.

Introduction data mining case studies

Cascading simplifies the integration of Hadoop with external ad systems. AWS infrastructure helps Razorfish reliably store and process huge Petabytes data sets. The Benefits The AWS elastic infrastructure platform allows Razorfish to manage wide variability in load by provisioning and removing capacity as needed.

We completed development and testing of our first client project in six weeks. Our process is completely automated.Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

Data mining is an interdisciplinary subfield of computer science with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.

INTRODUCTION TO DATA MINING WITH CASE STUDIES - G. K. GUPTA - Google Books

Introduction to Data Mining with Case Studies [G. K. Gupta] on pfmlures.com *FREE* shipping on qualifying offers. The field of data mining provides techniques for automated discovery of valuable information from the accumulated data of computerized operations of enterprises.

This book offers a clear and comprehensive introduction to both data mining theory and practice. This book guides R users into data mining and helps data miners who use R in their work.

Introduction to Data Mining With Case Studies by Gupta, G. K - PDF Free Download

It provides a how-to method using R for data mining applications from academia to industry. Razorfish, a digital advertising and marketing firm, segments users and customers based on the collection and analysis of non-personally identifiable data from browsing pfmlures.com so requires applying data mining methods across historical click streams to identify effective segmentation and categorization algorithms and techniques.

Razorfish, a digital advertising and marketing firm, segments users and customers based on the collection and analysis of non-personally identifiable data from browsing pfmlures.com so requires applying data mining methods across historical click streams to identify effective segmentation and categorization algorithms and techniques.

This book guides R users into data mining and helps data miners who use R in their work. It provides a how-to method using R for data mining applications from academia to industry.

R and Data Mining: Examples and Case Studies - pfmlures.com: R and Data Mining