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Officials: Quick Access to Claims Data Could Deter Health Fraud – iHealthBeat

Officials: Quick Access to Claims Data Could Deter Health Fraud
iHealthBeat
The federal government’s multi-agency Health Care Fraud Prevention and Enforcement Action Teams, or HEAT, have achieved some success in using data-mining …
Microsoft Unveils Beta for Dryad Technology – eWeek

Microsoft Unveils Beta for Dryad Technology
eWeek
“These technologies allow you to process large volumes of data in many types of applications, including data-mining applications, image and stream …
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Using a Computer to Fight Medicare Fraud – Wall Street Journal

Using a Computer to Fight Medicare Fraud
Wall Street Journal
… and Enforcement Action Teams—led to high-profile successes, the data mining never matched the hype, members of those strike forces say privately. …
Complete Online Data Mining Services To Save Your Money – NewDesignWorld (press release)

Complete Online Data Mining Services To Save Your Money
NewDesignWorld (press release)
Here companies need to get the help of online web research and data mining specialists. At Online web research services, companies can get deep and accurate …
Data mining by governments, others stirs up critics – ScrippsNews

Data mining by governments, others stirs up critics
ScrippsNews
Supporters of data mining say that compiling and analyzing individuals’ public and private records is often necessary to maintain national safety. …
Data Mining and Predictive Analytics Software That’s Easy to Use – eCRM Guide

Data Mining and Predictive Analytics Software That’s Easy to Use
eCRM Guide
Software review: When it comes to business applications for data mining and predictive analytics, one thing is certain: Businesses want to …
Online sellers face a thinly veiled threat – Kansas City Star

Fox News

Online sellers face a thinly veiled threat
Kansas City Star
Industry hails sophisticated data mining as the rebirth of the personal shopper. The techniques give consumers the wisdom of crowds drawn from those with …
Summary Box: Report calls for Web privacy rightsLive 5 News
all 778 news articles »
VermontSeeks Supreme Court Review of Data Mining Decision – Thompson.com

VermontSeeks Supreme Court Review of Data Mining Decision
Thompson.com
Last month the US Court of Appeals for the 2nd Circuit struck down a section of the Vermont Pharmaceutical Data Mining Law prohibiting the sale or use of …
Data mining looks for clues to prevention, treatment of depression – FierceHealthIT

Data mining looks for clues to prevention, treatment of depression
FierceHealthIT
Business intelligence specialists in Australia are examining how to mine patient data and clinician notes in search of better ways to diagnose and treat …
and more »
‘Data Mining’ Gains Traction in Education – Education Week News

‘Data Mining’ Gains Traction in Education
Education Week News
The new and rapidly growing field of educational data mining is using the chaff from data collected through normal school activities to …

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Data Miner Survey Report for 2009

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According to the 2009 Rexer Analytics Data Miner Surveys, in which 710 data miners from 58 countries participated, for the past 3 years data miners concluded that most commonly used algorithms are regression, decision trees, and cluster analysis. The report also mentioned that almost half of industry data miners rate the analytic capabilities of their company as above average or excellent. But 19% feel their company has minimal or no analytic capabilities.

Among other key points of the survey are:

IBM SPSS Modeler (SPSS Clementine), Statistica, and IBM SPSS Statistics (SPSS Statistics) are identified as the “primary tools” used by the most data miners.
Open-source tools Weka and R made substantial movement up data miner’s tool rankings this year, and are now used by large numbers of both academic and for-profit data miners.
SAS Enterprise Miner dropped in data miner’s tool rankings this year.
Users of IBM SPSS Modeler, Statistica, and Rapid Miner are the most satisfied with their software.
Fields & Industries: CRM / Marketing, Academic, Financial Services, & IT / Telecom. For-profit sector, the departments data miners most frequently work in are Marketing & Sales and Research & Development.

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Data Mining, Data Mining Trends

Screen Scraping vs Data Mining vs Web Mining

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I know the topic got many ‘vs’ but I want to highlight the differences between all of them together. Currently there is a video on YouTube titled “screen scraping, web data mining, web data scraping” and I am calling to clarify the misleading topic. You can watch the video below:

Read my posts about “Data Mining vs Screen-Scraping” and “Data Mining vs Web Mining” to get the whole idea of the topic. I just want to highlight some of the main differences as below:

Screen scraping was used to extract characters from the screens so that they could be analyzed. Screen scraping now most commonly refers to extracting information from web sites. That is, computer programs can “crawl” or “spider” through web sites, pulling out data. People often do this to build things like comparison shopping engines, archive web pages, or simply download text to a spreadsheet so that it can be filtered and analyzed.

Data mining, is defined by Wikipedia as the “practice of automatically searching large stores of data for patterns.” In other words, you already have the data, and you’re now analyzing it to learn useful things about it. Data mining often involves lots of complex algorithms based on statistical methods. It has nothing to do with how you got the data in the first place. In data mining you only care about analyzing what’s already there.

Web mining, on the other hand, is the application of data mining techniques to discover patterns from the Web. According to analysis targets, web mining can be divided into three different types, which are Web usage mining, Web content mining and Web structure mining.

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Top 10 Data Mining Mistakes

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Maybe some of you have read this white paper before, but I just want to add here as resource collection for future data mining beginners. The paper is a book excerpts from “Handbook of Statistical Analysis and Data Mining Applications“, Elsevier (ISBN: 978-0-123747655). According to the authors, mining data to extract useful and enduring patterns remains a skill arguably more art than science itself. In the paper, they briefly describe, and illustrate from examples, what they believe are the “Top 10” mistakes of data mining, in terms of frequency and seriousness.

Top 10 DM Mistakes (white paper)

0. Lack of Data (important too!)
1. Focus on Training
2. Rely on One Technique
3. Ask the Wrong Question
4. Listen (Only) to the Data
5. Accept Leaks from the Future
6. Discount Pesky Cases
7. Extrapolate
8. Answer Every Inquiry
9. Sample Casually
10. Believe the Best Model

I would like to emphasize on mistake no. 2 (Rely on 1 technique only) which I think is important for us to consider. In data mining task, it is important that we try variations of modeling algorithms to make sure that we get the best result. Find new algorithms/tools that are available in the market (sometimes it is good to read new publication in conference/journal to get latest improvement of the algorithms) to mine your data. There is a popular folklore “No Free Lunch” (NFL Theorem) that states no algorithm is better to solve all the problems!

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