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send email by R

For automated report delivery I have often used send email options in BASE SAS. For R, for scheduling tasks and sending me automated mails on completion of tasks I have two R options and 1 Windows OS scheduling option. Note red font denotes the parameters that should be changed. Anything else should NOT be changed.

Option 1-

Use the mail package at

http://cran.r-project.org/web/packages/mail/mail.pdf

> library(mail)

Attaching package: ‘mail’

The following object(s) are masked from ‘package:sendmailR’:

sendmail

>
> sendmail(“ohri2007@gmail.com“, subject=”Notification from R“,message=“Calculation finished!”, password=”rmail”)
[1] “Message was sent to ohri2007@gmail.com! You have 19 messages left.”

Disadvantage- Only 20 email messages by IP address per day. (but thats ok!)

Option 2-

use sendmailR package at http://cran.r-project.org/web/packages/sendmailR/sendmailR.pdf

install.packages()
library(sendmailR)
from <- sprintf(“<sendmailR@%s>”, Sys.info()[4])
to <- “<ohri2007@gmail.com>”
subject <- “Hello from R
body <- list(“It works!”, mime_part(iris))
sendmail(from, to, subject, body,control=list(smtpServer=”ASPMX.L.GOOGLE.COM”))

 

 

BiocInstaller version 1.2.1, ?biocLite for help
> install.packages(“sendmailR”)
Installing package(s) into ‘/home/ubuntu/R/library’
(as ‘lib’ is unspecified)
also installing the dependency ‘base64’

trying URL ‘http://cran.at.r-project.org/src/contrib/base64_1.1.tar.gz&#8217;
Content type ‘application/x-gzip’ length 61109 bytes (59 Kb)
opened URL
==================================================
downloaded 59 Kb

trying URL ‘http://cran.at.r-project.org/src/contrib/sendmailR_1.1-1.tar.gz&#8217;
Content type ‘application/x-gzip’ length 6399 bytes
opened URL
==================================================
downloaded 6399 bytes

BiocInstaller version 1.2.1, ?biocLite for help
* installing *source* package ‘base64’ …
** package ‘base64’ successfully unpacked and MD5 sums checked
** libs
gcc -std=gnu99 -I/usr/local/lib64/R/include -I/usr/local/include -fpic -g -O2 -c base64.c -o base64.o
gcc -std=gnu99 -shared -L/usr/local/lib64 -o base64.so base64.o -L/usr/local/lib64/R/lib -lR
installing to /home/ubuntu/R/library/base64/libs
** R
** preparing package for lazy loading
** help
*** installing help indices
** building package indices …
** testing if installed package can be loaded
BiocInstaller version 1.2.1, ?biocLite for help

* DONE (base64)
BiocInstaller version 1.2.1, ?biocLite for help
* installing *source* package ‘sendmailR’ …
** package ‘sendmailR’ successfully unpacked and MD5 sums checked
** R
** preparing package for lazy loading
** help
*** installing help indices
** building package indices …
** testing if installed package can be loaded
BiocInstaller version 1.2.1, ?biocLite for help

* DONE (sendmailR)

The downloaded packages are in
‘/tmp/RtmpsM222s/downloaded_packages’
> library(sendmailR)
Loading required package: base64
> from <- sprintf(“<sendmailR@%s>”, Sys.info()[4])
> to <- “<ohri2007@gmail.com>”
> subject <- “Hello from R”
> body <- list(“It works!”, mime_part(iris))
> sendmail(from, to, subject, body,
+ control=list(smtpServer=”ASPMX.L.GOOGLE.COM”))
$code
[1] “221”

$msg
[1] “2.0.0 closing connection ff2si17226764qab.40″

Disadvantage-This worked when I used the Amazon Cloud using the BioConductor AMI (for free 2 hours) at http://www.bioconductor.org/help/cloud/

It did NOT work when I tried it use it from my Windows 7 Home Premium PC from my Indian ISP (!!) .

It gave me this error

or in wait_for(250) :
SMTP Error: 5.7.1 [180.215.172.252] The IP you’re using to send mail is not authorized

 

PAUSE–

ps Why do this (send email by R)?

Note you can add either of the two programs of the end of the code that you want to be notified automatically. (like daily tasks)

This is mostly done for repeated business analytics tasks (like reports and analysis that need to be run at specific periods of time)

pps- What else can I do with this?

Can be modified to include sms or tweets  or even blog by email by modifying the   “to”  location appropriately.

3) Using Windows Task Scheduler to run R codes automatically (either the above)

or just sending an email

got to Start>  All Programs > Accessories >System Tools > Task Scheduler ( or by default C:Windowssystem32taskschd.msc)

Create a basic task

Now you can use this to run your daily/or scheduled R code  or you can send yourself email as well.

and modify the parameters- note the SMTP server (you can use the ones for google in example 2 at ASPMX.L.GOOGLE.COM)

and check if it works!

 

Related

 Geeky Things , Bro

-

Configuring IIS on your Windows 7 Home Edition-

note path to do this is-

Control Panel>All Control Panel Items> Program and Features>Turn Windows features on or off> Internet Information Services

and

http://stackoverflow.com/questions/709635/sending-mail-from-batch-file

 

JMP 10 released

JMP , the visual data exploration, statistical quality control software from SAS Institute launched version 10 of its software today.

Source-http://jmp.com/about/events/webcasts/jmp_webcast.shtml?name=jmp10

JMP 10 includes:

Numerous enhancements to the drag-and-drop Graph Builder, including a new iPad application.

A cutting-edge Control Chart Builder to create process control charts with drag-and-drop ease.

New reliability capabilities, including growth and forecast models.

Additions and improvements for sorting and filtering data, design of experiments, statistical modeling, scripting, add-in and application development, script debugging and more.

From JohnSall’s blog post at http://blogs.sas.com/content/jmp/2012/03/20/discover-more-with-jmp-10/

Much of the development centered on four focus areas:

1. Graph Builder everywhere. The Graph Builder platform itself has new features like Heatmap and Treemap, an elements palette and properties panel, making the choices more visible. But Graph Builder also has some descendents now, including the new Control Chart Builder, which makes creating control charts an interactive process. In addition, some of the drag-and-drop features that are used to change columns in Graph Builder are also available in Distribution, Fit Y by X, and a few other places. Finally, Graph Builder has been ported to the iPad. For the first time, you can use JMP for exploration and presentation on a mobile device for free. So just think of Graph Builder as gradually taking over in lots of places.

2. Expert-driven design.reliability, measurement systems, and partial least squares analyses.

3. Performance.  this release has the most new multithreading so far

4. Application development

You can read more here -http://jmp.com/about/events/webcasts/jmpwebcast_detail.shtml?reglink=70130000001r9IP

Facebook and R

Part 1 How do people at Facebook use R?

tamar Rosenn, Facebook

Itamar conveyed how Facebook’s Data Team used R in 2007 to answer two questions about new users: (i) which data points predict whether a user will stay? and (ii) if they stay, which data points predict how active they’ll be after three months?

For the first question, Itamar’s team used recursive partitioning (via the rpartpackage) to infer that just two data points are significantly predictive of whether a user remains on Facebook: (i) having more than one session as a new user, and (ii) entering basic profile information.

For the second question, they fit the data to a logistic model using a least angle regression approach (via the lars package), and found that activity at three months was predicted by variables related to three classes of behavior: (i) how often a user was reached out to by others, (ii) frequency of third party application use, and (iii) what Itamar termed “receptiveness” — related to how forthcoming a user was on the site.

source-http://www.dataspora.com/2009/02/predictive-analytics-using-r/

and cute graphs like the famous

https://www.facebook.com/notes/facebook-engineering/visualizing-friendships/469716398919

 

and

studying baseball on facebook

https://www.facebook.com/notes/facebook-data-team/baseball-on-facebook/10150142265858859

by counting the number of posts that occurred the day after a team lost divided by the total number of wins, since losses for great teams are remarkable and since winning teams’ fans just post more.

 

But mostly at

https://www.facebook.com/data?sk=notes and https://www.facebook.com/data?v=app_4949752878

 

and creating new packages

1. jjplot (not much action here!)

https://r-forge.r-project.org/scm/viewvc.php/?root=jjplot

though

I liked the promise of JJplot at

http://pleasescoopme.com/2010/03/31/using-jjplot-to-explore-tipping-behavior/

2. ising models

https://github.com/slycoder/Rflim

https://www.facebook.com/note.php?note_id=10150359708746212

3. R pipe

https://github.com/slycoder/Rpipe

 

even the FB interns are cool

http://brenocon.com/blog/2009/02/comparison-of-data-analysis-packages-r-matlab-scipy-excel-sas-spss-stata/

 

Part 2 How do people with R use Facebook?

Using the API at https://developers.facebook.com/tools/explorer

and code mashes from

 

http://romainfrancois.blog.free.fr/index.php?post/2012/01/15/Crawling-facebook-with-R

http://applyr.blogspot.in/2012/01/mining-facebook-data-most-liked-status.html

but the wonderful troubleshooting code from http://www.brocktibert.com/blog/2012/01/19/358/

which needs to be added to the code first

 

and using network package

>access_token=”XXXXXXXXXXXX”

Annoyingly the Facebook token can expire after some time, this can lead to huge wait and NULL results with Oauth errors

If that happens you need to regenerate the token

What we need
> require(RCurl)
> require(rjson)
> download.file(url=”http://curl.haxx.se/ca/cacert.pem&#8221;, destfile=”cacert.pem”)

Roman’s Famous Facebook Function (altered)

> facebook <- function( path = “me”, access_token , options){
+ if( !missing(options) ){
+ options <- sprintf( “?%s”, paste( names(options), “=”, unlist(options), collapse = “&”, sep = “” ) )
+ } else {
+ options <- “”
+ }
+ data <- getURL( sprintf( “https://graph.facebook.com/%s%s&access_token=%s&#8221;, path, options, access_token ), cainfo=”cacert.pem” )
+ fromJSON( data )
+ }

 

Now getting the friends list
> friends <- facebook( path=”me/friends” , access_token=access_token)
> # extract Facebook IDs
> friends.id <- sapply(friends$data, function(x) x$id)
> # extract names
> friends.name <- sapply(friends$data, function(x) iconv(x$name,”UTF-8″,”ASCII//TRANSLIT”))
> # short names to initials
> initials <- function(x) paste(substr(x,1,1), collapse=””)
> friends.initial <- sapply(strsplit(friends.name,” “), initials)

This matrix can take a long time to build, so you can change the value of N to say 40 to test your network. I needed to press the escape button to cut short the plotting of all 400 friends of mine.
> # friendship relation matrix
> N <- length(friends.id)
> friendship.matrix <- matrix(0,N,N)
> for (i in 1:N) {
+ tmp <- facebook( path=paste(“me/mutualfriends”, friends.id[i], sep=”/”) , access_token=access_token)
+ mutualfriends <- sapply(tmp$data, function(x) x$id)
+ friendship.matrix[i,friends.id %in% mutualfriends] <- 1
+ }

 

Plotting using Network package in R (with help from the  comments at http://applyr.blogspot.in/2012/01/mining-facebook-data-most-liked-status.html)

> require(network)

>net1<- as.network(friendship.matrix)

> plot(net1, label=friends.initial, arrowhead.cex=0)

(Rgraphviz is tough if you are on Windows 7 like me)

but there is an alternative igraph solution at https://github.com/sciruela/facebookFriends/blob/master/facebook.r

 

After all that-..talk.. a graph..of my Facebook Network with friends initials as labels..

 

Opinion piece-

I hope plans to make the Facebook R package get fulfilled (just as the twitteR  package led to many interesting analysis)

and also Linkedin has an API at http://developer.linkedin.com/apis

I think it would be interesting to plot professional relationships across social networks as well. But I hope to see a LinkedIn package (or blog code) soon.

As for jjplot, I had hoped ggplot and jjplot merged or atleast had some kind of inclusion in the Deducer GUI. Maybe a Google Summer of Code project if people are busy!!

Also the geeks at Facebook.com can think of giving something back to the R community, as Google generously does with funding packages like RUnit, Deducer and Summer of Code, besides sponsoring meet ups etc.

 

(note – this is part of the research for the upcoming book ” R for Business Analytics”)

 

ps-

but didnt get time to download all my posts using R code at

https://gist.github.com/1634662#

or do specific Facebook Page analysis using R at

http://tonybreyal.wordpress.com/2012/01/06/r-web-scraping-r-bloggers-facebook-page-to-gain-further-information-about-an-authors-r-blog-posts-e-g-number-of-likes-comments-shares-etc/

Updated-

 #access token from https://developers.facebook.com/tools/explorer
access_token="AAuFgaOcVaUZAssCvL9dPbZCjghTEwwhNxZAwpLdZCbw6xw7gARYoWnPHxihO1DcJgSSahd67LgZDZD"
require(RCurl)
 require(rjson)
# download the file needed for authentication http://www.brocktibert.com/blog/2012/01/19/358/
download.file(url="http://curl.haxx.se/ca/cacert.pem", destfile="cacert.pem")
# http://romainfrancois.blog.free.fr/index.php?post/2012/01/15/Crawling-facebook-with-R
facebook <- function( path = "me", access_token = token, options){
if( !missing(options) ){
options <- sprintf( "?%s", paste( names(options), "=", unlist(options), collapse = "&", sep = "" ) )
} else {
options <- ""
}
data <- getURL( sprintf( "https://graph.facebook.com/%s%s&access_token=%s", path, options, access_token ), cainfo="cacert.pem" )
fromJSON( data )
}

 # see http://applyr.blogspot.in/2012/01/mining-facebook-data-most-liked-status.html

# scrape the list of friends
friends <- facebook( path="me/friends" , access_token=access_token)
# extract Facebook IDs
friends.id <- sapply(friends$data, function(x) x$id)
# extract names 
friends.name <- sapply(friends$data, function(x)  iconv(x$name,"UTF-8","ASCII//TRANSLIT"))
# short names to initials 
initials <- function(x) paste(substr(x,1,1), collapse="")
friends.initial <- sapply(strsplit(friends.name," "), initials)

# friendship relation matrix
#N <- length(friends.id)
N <- 200
friendship.matrix <- matrix(0,N,N)
for (i in 1:N) {
  tmp <- facebook( path=paste("me/mutualfriends", friends.id[i], sep="/") , access_token=access_token)
  mutualfriends <- sapply(tmp$data, function(x) x$id)
  friendship.matrix[i,friends.id %in% mutualfriends] <- 1
}
require(network)
net1<- as.network(friendship.matrix)
plot(net1, label=friends.initial, arrowhead.cex=0)

Created by Pretty R at inside-R.org

Predictive Models Ain’t Easy to Deploy

 

This is a guest blog post by Carole Ann Matignon of Sparkling Logic. You can see more on Sparkling Logic at http://my.sparklinglogic.com/

Decision Management is about combining predictive models and business rules to automate decisions for your business. Insurance underwriting, loan origination or workout, claims processing are all very good use cases for that discipline… But there is a hiccup… It ain’t as easy you would expect…

What’s easy?

If you have a neat model, then most tools would allow you to export it as a PMML model – PMML stands for Predictive Model Markup Language and is a standard XML representation for predictive model formulas. Many model development tools let you export it without much effort. Many BRMS – Business rules Management Systems – let you import it. Tada… The model is ready for deployment.

What’s hard?

The problem that we keep seeing over and over in the industry is the issue around variables.

Those neat predictive models are formulas based on variables that may or may not exist as is in your object model. When the variable is itself a formula based on the object model, like the min, max or sum of Dollar amount spent in Groceries in the past 3 months, and the object model comes with transaction details, such that you can compute it by iterating through those transactions, then the problem is not “that” big. PMML 4 introduced some support for those variables.

The issue that is not easy to fix, and yet quite frequent, is when the model development data model does not resemble the operational one. Your Data Warehouse very likely flattened the object model, and pre-computed some aggregations that make the mapping very hard to restore.

It is clearly not an impossible project as many organizations do that today. It comes with a significant overhead though that forces modelers to involve IT resources to extract the right data for the model to be operationalized. It is a heavy process that is well justified for heavy-duty models that were developed over a period of time, with a significant ROI.

This is a show-stopper though for other initiatives which do not have the same ROI, or would require too frequent model refresh to be viable. Here, I refer to “real” model refresh that involves a model reengineering, not just a re-weighting of the same variables.

For those initiatives where time is of the essence, the challenge will be to bring closer those two worlds, the modelers and the business rules experts, in order to streamline the development AND deployment of analytics beyond the model formula. The great opportunity I see is the potential for a better and coordinated tuning of the cut-off rules in the context of the model refinement. In other words: the opportunity to refine the strategy as a whole. Very ambitious? I don’t think so.

About Carole Ann Matignon

http://my.sparklinglogic.com/index.php/company/management-team

Carole-Ann Matignon Print E-mail

Carole-Ann MatignonCarole-Ann Matignon – Co-Founder, President & Chief Executive Officer

She is a renowned guru in the Decision Management space. She created the vision for Decision Management that is widely adopted now in the industry.  Her claim to fame is managing the strategy and direction of Blaze Advisor, the leading BRMS product, while she also managed all the Decision Management tools at FICO (business rules, predictive analytics and optimization). She has a vision for Decision Management both as a technology and a discipline that can revolutionize the way corporations do business, and will never get tired of painting that vision for her audience.  She speaks often at Industry conferences and has conducted university classes in France and Washington DC.

She started her career building advanced systems using all kinds of technologies — expert systems, rules, optimization, dashboarding and cubes, web search, and beta version of database replication. At Cleversys (acquired by Kurt Salmon & Associates), she also conducted strategic consulting gigs around change management.

While playing with advanced software components, she found a passion for technology and joined ILOG (acquired by IBM). She developed a growing interest in Optimization as well as Business Rules. At ILOG, she coined the term BRMS while brainstorming with her Sales counterpart. She led the Presales organization for Telecom in the Americas up until 2000 when she joined Blaze Software (acquired by Brokat Technologies, HNC Software and finally FICO).

Her 360-degree experience allowed her to gain appreciation for all aspects of a software company, giving her a unique perspective on the business. Her technical background kept her very much in touch with technology as she advanced.

How to find out people who are spamming you

Step 1-

We assume you have Gmail. If you dont have Gmail, you deserve the Spam

You click -show original on the drop down in the spammy message

 

you see a lot of mumbo jumbo

(or you just pick the IP addresses from comment spam)

Step 2-

You pick the IP addresses from the mumbo jumbo above (called headers )

http://en.wikipedia.org/wiki/IP_address

An Internet Protocol address (IP address) is a numerical label assigned to each device (e.g., computer, printer) participating in a computer networkthat uses the Internet Protocol for communication.[1] An IP address serves two principal functions: host or network interface identification and locationaddressing

Step 3-

You find out who has that IP address using arin

https://www.arin.net/

 

Step 4-

You put those IP addresses in your firewall for your computer

http://technet.microsoft.com/en-us/library/cc733090(v=ws.10).aspx

(or if you have a self-hosted blog using Website cpanel ip deny)

http://www.siteground.com/tutorials/cpanel/ip_deny_manager.htm

Step 5-

 

Communicate to that IP Address using IRC

http://en.wikipedia.org/wiki/Internet_Relay_Chat

Internet Relay Chat (IRC) is a protocol for real-time Internet text messaging (chat) or synchronous conferencing.[1] It is mainly designed for group communication in discussion forums, called channels,[2] but also allows one-to-one communication via private message[3] as well as chat and data transfer,[4] including file sharing.[5]

or use HOIC to test your own firewall better before people  spam  you

http://gizmodo.com/5883146/what-is-hoic or

http://www.decisionstats.com/occupy-the-internet/

 

Top 5 XKCD on Data Visualization

By request, an analysis of Top 5  XKCDs on data visualization. Statisticians and Data Scientists to note-

1) DOT PLOT

 

2)  LINE PLOTS

3) FLOW CHARTS

4) PIE CHARTS and 5) BAR GRAPHS

I am not going into the big big graphs of course like the Star Wars Plot data visualization at

http://xkcd.com/657/ or the Money Chart at http://xkcd.com/980/ because I dont believe in data visualization to show off, but to keep it simple simply :)

Now I gotta find me a software that can write my blog for me :)

Analytics for Cyber Conflict

 

The emerging use of Analytics and Knowledge Discovery in Databases for Cyber Conflict and Trade Negotiations

 

The blog post is the first in series or articles on cyber conflict and the use of analytics for targeting in both offense and defense in conflict situations.

 

It covers knowledge discovery in four kinds of databases (so chosen because of perceived importance , sensitivity, criticality and functioning of the geopolitical economic system)-

  1. Databases on Unique Identity Identifiers- including next generation biometric databases connected to Government Initiatives and Banking, and current generation databases of identifiers like government issued documents made online
  2. Databases on financial details -This includes not only traditional financial service providers but also online databases with payment details collected by retail product selling corporates like Sony’s Playstation Network, Microsoft ‘s XBox and
  3. Databases on contact details – including those by offline businesses collecting marketing databases and contact details
  4. Databases on social behavior- primarily collected by online businesses like Facebook , and other social media platforms.

It examines the role of

  1. voluntary privacy safeguards and government regulations ,

  2. weak cryptographic security of databases,

  3. weakness in balancing marketing ( maximized data ) with privacy (minimized data)

  4. and lastly the role of ownership patterns in database owning corporates

A small distinction between cyber crime and cyber conflict is that while cyber crime focusses on stealing data, intellectual property and information  to primarily maximize economic gains

cyber conflict focuses on stealing information and also disrupt effective working of database backed systems in order to gain notional competitive advantages in economics as well as geo-politics. Cyber terrorism is basically cyber conflict by non-state agents or by designated terrorist states as defined by the regulations of the “target” entity. A cyber attack is an offensive action related to cyber-infrastructure (like the Stuxnet worm that disabled uranium enrichment centrifuges of Iran). Cyber attacks and cyber terrorism are out of scope of this paper, we will concentrate on cyber conflicts involving databases.

Some examples are given here-

Types of Knowledge Discovery in -

1) Databases on Unique Identifiers- including biometric databases.

Unique Identifiers or primary keys for identifying people are critical for any intensive knowledge discovery program. The unique identifier generated must be extremely secure , and not liable to reverse engineering of the cryptographic hash function.

For biometric databases, an interesting possibility could be determining the ethnic identity from biometric information, and also mapping relatives. Current biometric information that is collected is- fingerprint data, eyes iris data, facial data. A further feature could be adding in voice data as a part of biometric databases.

This is subject to obvious privacy safeguards.

For example, Google recently unveiled facial recognition to unlock Android 4.0 mobiles, only to find out that the security feature could easily be bypassed by using a photo of the owner.

 

 

Example of Biometric Databases

In Afghanistan more than 2 million Afghans have contributed iris, fingerprint, facial data to a biometric database. In India, 121 million people have already been enrolled in the largest biometric database in the world. More than half a million customers of the Tokyo Mitsubishi Bank are are already using biometric verification at ATMs.

Examples of Breached Online Databases

In 2011, Playstation Network by Sony (PSN) lost data of 77 million customers including personal information and credit card information. Additionally data of 24 million customers were lost by Sony’s Sony Online Entertainment. The websites of open source platforms like SourceForge, WineHQ and Kernel.org were also broken into 2011. Even retailers like McDonald and Walgreen reported database breaches.

 

The role of cyber conflict arises in the following cases-

  1. Databases are online for accessing and authentication by proper users. Databases can be breached remotely by non-owners ( or “perpetrators”) non with much lesser chance of intruder identification, detection and penalization by regulators, or law enforcers (or “protectors”) than offline modes of intellectual property theft.

  2. Databases are valuable to external agents (or “sponsors”) subsidizing ( with finance, technology, information, motivation) the perpetrators for intellectual property theft. Databases contain information that can be used to disrupt the functioning of a particular economy, corporation (or “ primary targets”) or for further chain or domino effects in accessing other data (or “secondary targets”)

  3. Loss of data is more expensive than enhanced cost of security to database owners

  4. Loss of data is more disruptive to people whose data is contained within the database (or “customers”)

So the role play for different people for these kind of databases consists of-

1) Customers- who are in the database

2) Owners -who own the database. They together form the primary and secondary targets.

3) Protectors- who help customers and owners secure the databases.

and

1) Sponsors- who benefit from the theft or disruption of the database

2) Perpetrators- who execute the actual theft and disruption in the database

The use of topic models and LDA is known for making data reduction on text, and the use of data visualization including tied to GPS based location data is well known for investigative purposes, but the increasing complexity of both data generation and the sophistication of machine learning driven data processing makes this an interesting area to watch.

 

 

The next article in this series will cover-

the kind of algorithms that are currently or being proposed for cyber conflict, the role of non state agents , and what precautions can knowledge discovery in databases practitioners employ to avoid breaches of security, ethics, and regulation.

Citations-

  1. Michael A. Vatis , CYBER ATTACKS DURING THE WAR ON TERRORISM: A PREDICTIVE ANALYSIS Dartmouth College (Institute for Security Technology Studies).
  2. From Data Mining to Knowledge Discovery in Databases Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyt
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