Online social networks usage are pervasive now a days. Mining the text present in online social networks will be useful for predictive analytic. Predicting information from unstructured data present in the social networks is a challenging research problem. Extracting, identifying or otherwise characterizing the sentiment content of the text unit using statistics and machine learning methods are...

Weka4WS adopts the emerging Web Services Resource Framework (WSRF) for accessing remote data mining algorithms and managing distributed computations. The Weka4WS user interface is a modified Weka Explorer environment that supports the execution of both local and remote data mining tasks. Workflow environments are widely used in data mining systems to manage data and execution flows associated t...

Enormous quantities of trajectory data are collected from many sources, as GPS devices and mobile phones, as sequences of points. These data can be used in many application domains such as traffic management, urban planing, tourism, and bird migration. However, in most applications a higher level of abstraction should be used instead of sample points. In this paper we present an extension of th...

Abstract This work shows the use of WEKA , a tool that implements most common machine learning algorithms, to perform Text Mining analysis on set documents. Applying these methods requires initial steps where text is converted into structured format. Both processing phase and transformed dataset, using classification clustering can be carried out entirely with this tool, in rigorous simple way....

This paper presents Weka4WS, a framework that extends the Weka toolkit for supporting distributed data mining on Grid environments. Weka4WS adopts the emerging Web Services Resource Framework (WSRF) for accessing remote data mining algorithms and managing distributed computations. The Weka4WS user interface is a modified Weka Explorer environment that supports the execution of both local and re...

Data mining (also known as knowledge discovery from databases) is the process of extraction of hidden, previously unknown and potentially useful information from databases. The outcome of the extracted data can be analyzed for the future planning and development perspectives. In this paper, we have made an attempt to demonstrate how one can extract the local (district) level census, socio-econo...

This paper presents a new Windows®-based software utility for WEKA, a data mining software workbench, to simplify large-scale experiment and evaluation with many algorithms and datasets in the classification context. The proposed tool, LEET (Large Experiment and Evaluation Tool) makes it possible to accomplish a variety of tasks that are presently rather difficult or impractical through the sta...

The Waikato Environment for Knowledge Analysis (WEKA) came about through the perceived need for a unified workbench that would allow researchers easy access to state-of the-art techniques in machine learning algorithms for data mining tasks. It provides a general-purpose environment for automatic classification, regression, clustering, and feature selection etc. in various research areas. This ...

Machine learning and Data mining are becoming increasingly important in the recent years and have been successfully applied to solve a number of problems, especially in the areas of science and engineering. A variety of algorithms exist in the popular data mining tool ‘Weka’ to classify a given set of records into different classes. However, choosing the right classifier among them is a tricky ...

The classification of organisms is a daily-basis task in biology as well as other contexts. This process is usually carried out by comparing a set of descriptors associated with each object. However, general-purpose statistical packages offer a limited number of methods to perform such a comparison, and specific tools are required for each concrete problem. Weka is a freely-available framework ...