RapidMiner

Financial impact is one of the real business effects of poor information quality as this includes diminish in income which thus diminishes the benefit of the organization and increment in working expenses. Dishonorable Decisions Any organization take business choices in view of the information that they hold, and have poor information quality puts the business at incredible hazard since the choices taken won't be the correct one and would cost the organization. There will be colossal effect in the

R( ) Python RapidMiner purpose R emphases on data analysis, statistics and graphical models Python focuses on efficiency and code readability It offers a unique mix of power, analytics capabilities and versatility Used By R is more preferred by statistician and data scientist Python is preferred by programmers who want to analysis or perform statistical techniques RapidMiner is preferred by who neither statistician nor programmer. Mostly business

(3)Discussion and analysis: R-R can be install from https://cran.r-project.org/mirrors.html. Current binary versions of R run on Windows 7 or later, including on 64-bit versions. RStudio is the primary IDE for R and can be install from https://www.rstudio.com/products/RStudio/. Rstudio is just formed for R. Some of features are: • Runs on all major platforms like Windows, Mac, and Linux. It can likewise be keep running as a server, enabling multiple users to access the RStudio IDE using a web browser

There are several improvement methods are available to improve decision tree performance in terms of accuracy, and modelling time. Since experimenting with every available method is impossible, some of the methods are selected that are proven to increase decision tree performances. Selected improvement methods and their experimental setups are presented in this chapter. 4.1 Correlation-Based Feature Selection Feature selection is a method used for reducing number of dimensions of a dataset

as data preprocessing, association rules , classification, regression, clustering, and information visualization. • Massive Online Analysis (MOA): This is based on the WEKA framework that is build and designed for data stream learning. • RapidMiner: RapidMiner is another importantopen source software for data mining. 2) Some important clustering algorithms discussed in this paper to group massive data and can be useful to industries and organization: • Partitioning methods: This algorithm groups

Additionally, I was able to learn how to use RapidMiner to clean and analyze the data to determine the confidence level that a user would purchase a eReader. For example, for the user ID of 19889, we can say with a confidence score of 0.410 that the user would be an early adopter of the product. Moreover

4.6 Big Data Issues, and Challenges There are many fundamental issue areas that need to be addressed in dealing with big data: data acquisition, data storage, data transfer, data management, and data processing. Each of these issues represents a large set of technical research problems and challenges in its own right. 1. Data acquisition Variety of data sources and the huge volumes of data make accessing data and data acquisition major issue and big challenge needs more research attentions. 2

Introduction: Content mining is tied in with recovering important data from information or content accessible in accumulations of reports through the recognizable proof and investigation of fascinating examples. This procedure is particularly beneficial when a client needs to locate a specific kind of incredible data on the web. Content mining is focusing on the record accumulation. The greater part of content mining calculation and methods are gone for finding designs crosswise over extensive record

An Enhanced Approach for Web Services Clustering using Supervised Machine Learning Techniques ABSTRACT Automatic document classification provides techniques that may improve and support web service clustering. As the number of services increases, the cost of classifying services through manual work increases. In this research, we presented an enhanced approach for service clustering that combines text mining and machine learning technology. The method only uses text description of each service

emphasize the importance of data mining to realize the value of data warehouse. 2. DATA MINING TOOLS There are plenty of data mining tools available in the market and most of the seller also provides the demo or freeware version. Some of them are RapidMiner, WEKA, R-Programming, Orange, KNIME, NLTK etc (http://thenewstack.io/six-of-the-best-open-source-data-mining-tools/) 2.1