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5 of the Best Free and Open Source Data Mining Software

5 of the Best Free and Open Source Data Mining Software
The process of extracting patterns from data is called data mining. It is recognized as an essential tool by modern business since it is able to convert data into business intelligence thus giving an informational edge. At present, it is widely used in profiling practices, like surveillance, marketing, scientific discovery, and fraud detection. There are four kinds of tasks that are normally involve in Data mining: * Classification - the task of generalizing familiar structure to employ to new data* Clustering - the task of finding groups and structures in the data that are in some way or another the same, without using noted structures in the data.* Association rule learning - Looks for relationships between variables.* Regression - Aims to find a function that models the data with the slightest error. For those of you who are looking for some data mining tools, here are five of the best open-source data mining software that you could get for free: Orange RapidMiner Weka JHepWork

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Weka 3 - Data Mining with Open Source Machine Learning Software in Java Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

Factor graph In probability theory and its applications, a factor graph is a particular type of graphical model, with applications in Bayesian inference, that enables efficient computation of marginal distributions through the sum-product algorithm. One of the important success stories of factor graphs and the sum-product algorithm is the decoding of capacity-approaching error-correcting codes, such as LDPC and turbo codes. A factor graph is an example of a hypergraph, in that an arrow (i.e., a factor node) can connect more than one (normal) node. When there are no free variables, the factor graph of a function f is equivalent to the constraint graph of f, which is an instance to a constraint satisfaction problem. Definition[edit] A factor graph is a bipartite graph representing the factorization of a function., My IP, And More IP Address Information Explained If you're looking for more information on IP addresses, you've come to the right place. is your source for a plethora of reliable information on IP addresses in addition to free IP lookup tools. Educate yourself on the terms of networking and the Internet! We like to make sure that you understand what you are looking at, so we have put together some helpful dictionary and commonly used terms here on so that you understand things better. You will find terms covered for IP address, how to hide my IP address, IP number, static and dynamic IPs, and private IP addresses.

Catalog The Socrata Open Data API (SODA) allows software developers to access data hosted in Socrata data sites programmatically. Developers can create applications that use the SODA APIs to visualize and “mash-up” Socrata datasets in new and exciting ways. Create an iPhone application that visualizes government spending in your area, a web application that allows citizens to look up potential government benefits they'd overlooked, or a service that automatically emails you when new earmarks are added to bills that you wish to track. To start accessing this dataset programmatically, use the API endpoint provided below. For more information and examples on how to use the Socrata Open Data API, reference our Developer Documentation. Eureqa Eureqa is a breakthrough technology that uncovers the intrinsic relationships hidden within complex data. Traditional machine learning techniques like neural networks and regression trees are capable tools for prediction, but become impractical when "solving the problem" involves understanding how you arrive at the answer. Eureqa uses a breakthrough machine learning technique called Symbolic Regression to unravel the intrinsic relationships in data and explain them as simple math.

Data Mining: Finding Similar Items and Users Because we want to give kick-ass product recommendations. I'm showing you how to find related items based on a really simple formula. If you pay attention, this technique is used all over the web (like on Amazon) to personalize the user experience and increase conversion rates. To get one question out of the way: there are already many available libraries that do this, but as you'll see there are multiple ways of skinning the cat and you won't be able to pick the right one without understanding the process, at least intuitively. Defining the Problem The 101 Most Useful Websites on the Internet Here are some of the most useful websites on the internet that you may not know about. These web sites, well most of them, solve at least one problem really well and they all have simple web addresses (URLs) that you can memorize thus saving you a trip to Google. And if you find this list useful, also check out the expanded version – The Most Useful Websites – which now offers a collection of 150+ undiscovered and incredibly useful websites to enhance your productivity. – for capturing screenshots of web pages on mobile and desktops. – online voice recognition in the browser itself. Changelog and Updates

Data science Data Science Data science is the study of the generalizable extraction of knowledge from data,[1] yet the key word is science.[2] It incorporates varying elements and builds on techniques and theories from many fields, including signal processing, mathematics, probability models, machine learning, computer programming, statistics, data engineering, pattern recognition and learning, visualization, uncertainty modeling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products. Data Science need not be always for big data, however, the fact that data is scaling up makes big data an important aspect of data science. A practitioner of data science is called a data scientist.

Data Mining, a useful tool in Business Intelligence In many occasions we have heard about Data Mining but, what is it exactly and when do we have to use it?. Well, I am going to start with some basis definitions I have collected from different sources and authors and I have made a nice combination (from my point of view) that I will share in this post. What is it? Data Mining is an extraction activity and its objective is discovering facts which are in the data base. In the same way it enables you to deduce hidden knowledge by examining or training the data. The knowledge founded is expressed in patterns and rules.

Bucket - XKCD Wiki Bucket has an outer shell of metal[citation needed]; within the metal is a protective layer of high density plastic[citation needed], in which may or may not reside pure HOH[citation needed]. There[citation needed] can only be speculation about what else the Bucket contains.[citation needed] Do not make our Bucket stupid or mean. Gmail's new compose and reply experience - Gmail Help You can now write messages in a cleaner, simpler experience that puts the focus on your message itself, not all the features around it. Here are some of the highlights: Fast: Compose messages right from your inbox. Simple: Redesigned with a clean, streamlined look. Powerful: Check emails as you're typing, minimize drafts for later, and even compose two messages at once.

machine learning in Python — scikit-learn 0.13.1 documentation "We use scikit-learn to support leading-edge basic research [...]" "I think it's the most well-designed ML package I've seen so far." "scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]." "For these tasks, we relied on the excellent scikit-learn package for Python." Data Mining Image: Detail of sliced visualization of thirty video samples of Downfall remixes. See actual visualization below. As part of my post doctoral research for The Department of Information Science and Media Studies at the University of Bergen, Norway, I am using cultural analytics techniques to analyze YouTube video remixes. My research is done in collaboration with the Software Studies Lab at the University of California, San Diego. A big thank you to CRCA at Calit2 for providing a space for daily work during my stays in San Diego. The following is an excerpt from an upcoming paper titled, “Modular Complexity and Remix: The Collapse of Time and Space into Search,” to be published in the peer review journal AnthroVision, Vol 1.1.

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