text mining of the project description data for the OSS projects. The dataset of project description for each of the project in its development phase was used in SAS Text Miner to create the new variable, called "project-type".
Text Mining, Big Data, Unstructured Data TIBCO® Statistica The purpose of Text Mining is to process unstructured (textual) information, extract meaningful numeric indices from the text, and, thus, make the information contained in the text accessible to the various data mining (statistical and machine learning) algorithms.
• SAS Text Mining Tutorial by Examples • From Text to Numbers: How to transform non-quantitative to quantitative We are using the classic example of stylometric investigation of the 77 Federalist Papers to explain SAS text mining step by step.
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It is one of the best text mining software to easily analyze text data from multiple sources. It has high-performance text mining feature which quickly evaluates data from large set of data collections.
IEEE Computer Special Issue on Data Mining, Vol. 32, No. 8, August 1999 Future Generation Computer Systems, Vol. 13, no. 2/3, Special Issue on Data Mining IEEE Transactions Knowledge and Data Engineering 8(6), December 1996, Special Section On Mining Of Databases.
SAS' integrated data mining and text mining capabilities uncover insights quickly from information contained in structured data as well as unstructured data in large document collections. Technical and business users alike will benefit from new, easy-to-use graphical reports.
SAS Global Forum 2011 Data Mining and Text Analytics Paper 157-2011 Data mining in SAS® with open source software Zhengping Ma, Eli Lilly and Company ABSTRACT It is common in many industries for data to exist in SAS format.
This paper benchmarks SAS and open-source products to analyze big data by modeling four classification problems from real customers. The products that were benchmarked are SAS Rapid Predictive Modeler (a component of SAS Enterprise Miner), SAS High-Performance Analytics Server (using Hadoop), R and
Our text mining software lets you easily analyze text data from the web, comment fields, books and other text sources. So, why limit yourself to analyzing legacy data? Deepen your understanding by discovering new information, topics and term relationships. And add what you learn to your models to improve lift and performance.
Data Mining Case Studies papers have greater latitude in (a) range of topics - authors may touch upon areas such as optimization, operations research, inventory control, and so on, (b) page length - longer submissions are allowed, (c) scope - more complete context, problem and
A number of texts introduce text mining to less technical audiences. Inmon and Nesavich (2007) provide a nontechnical overview that describes some of the history and applications of text mining. This book gives an overview of the procedures used to mine data, from preprocessing and integrating the text to using data mining procedures like self-organizing maps. Weiss, et al. (2005) provide a ...
Statistical Analysis System (SAS) is a product of SAS Institute developed for analytics & data management. SAS can mine data, alter it, manage data from different sources and perform statistical analysis. It provides a graphical UI for non-technical users.
I have another data set called Transaction which has text data describing about the transaction details. I need to categorize every row in the transaction data set into a category called "Restaurant" or "Other" based on the relationship between the terms contained within the description and the terms that I already have in the Restaurant data set.
Text Mining the Federalist Papers This demonstration illustrates how to use the Text Miner node to identify the author or authors of the 12 unattributed Federalist Papers. Some of the demonstrations in this course are "pre-cooked"; that is, a project diagram already exists in the course data that contains the complete demonstration. The instructor might use the pre-cooked version, or might ...
This paper compares the performance of Bayesian network classifiers to other popular classification methods such as classification tree, neural network, logistic regression, and support vector machines. It also shows some real-world applications of the implemented Bayesian network classifiers and a