Many organizations rely on spatial analysis to make business and agency decisions and to conduct research. The main difference between data mining in relational DBS and in spatial DBS is the interest of neighboring object’s attributes may have an influence on the current object, so the neighboring object have to be considered as well. The explicit location and extension of spatial objects defin...

In data mining classification is to accurately predict the target class for each case in the data. Decision tree algorithm is one of the commonly used classification algorithm to make induction learning based on examples. In this paper we present the comparison of different classification techniques using WEKA. The aim of this paper is to investigate the performance of different classification ...

Performing massive data mining experiments with multiple datasets and methods is a common task faced by most bioinformatics and computational biology laboratories. WEKA is a machine learning package designed to facilitate this task by providing tools that allow researchers to select from several classification methods and specific test strategies. Despite its popularity, the current WEKA enviro...

WEKA is a popular machine learning workbench with a development life of nearly two decades. This article provides an overview of the factors that we believe to be important to its success. Rather than focussing on the software’s functionality, we review aspects of project management and historical development decisions that likely had an impact on the uptake of the project.

Abstract— The various tasks like classification, clustering and association rule deriving are performed in the data-mining for the pattern extraction. The performance evaluation measures make each task distinct and meaningful. The plenty of machine learning algorithms helps in the different ways. The classification helps to predict about the future well in advance and make necessary actions thu...

Software cost estimation is the process of predicting the effort required to develop a software system. The basic input for the software cost estimation is coding size and set of cost drivers, the output is Effort in terms of Person-Months (PM’s). Here, the use of support vector regression (SVR) has been proposed for the estimation of software project effort. We have used the COCOMO dataset and...