Data Analysis Techniques in RapidMiner

Business Analytics

Group Project on

107124511845800

Table of Contents, Tables, and Figures

TOC \o "1-3" \h \z \u Introduction PAGEREF _Toc101613077 \h 3

Overview PAGEREF _Toc101613078 \h 3

Market Trend PAGEREF _Toc101613079 \h 4

Advantages and Limitations of RapidMiner PAGEREF _Toc101613080 \h 5

Advantages PAGEREF _Toc101613081 \h 5

Limitations PAGEREF _Toc101613082 \h 6

RapidMiner user interface PAGEREF _Toc101613083 \h 7

Example using RapidMiner (Building Linear Regression Model) PAGEREF _Toc101613084 \h 9

Importing the Dataset PAGEREF _Toc101613085 \h 9

Building Linear regression Process PAGEREF _Toc101613086 \h 9

Regression Model PAGEREF _Toc101613087 \h 11

Performance Vector PAGEREF _Toc101613088 \h 12

Example Set (Apply Model) PAGEREF _Toc101613089 \h 12

Reference PAGEREF _Toc101613090 \h 13

Appendix PAGEREF _Toc101613091 \h 14

TOC \h \z \c "Table" Table 1: RapidMiner and Competition PAGEREF _Toc101613092 \h 6

Table 2: RapidMiner Interface PAGEREF _Toc101613093 \h 9

TOC \h \z \c "Figure"

Figure 1: The Process PAGEREF _Toc101700935 \h 11

Figure 2: Linear Regression Model PAGEREF _Toc101700936 \h 11

Figure 3: Result view of the Model PAGEREF _Toc101700937 \h 12

Introduction

RapidMiner is a data and text mining software that is free, and accessible to all users as an open source for analysis. RapidMiner (earlier called YALE) was founded in the year 2001, by Ralph Klinkenberg, Simon Fischer, and Ingo Mierswa. In the year 2007 YALE was renamed to RapidMiner and the organization from Rapid-I to RapidMiner in 2013 (Lima, 2022).

RapidMiner works on operating systems of Linux, Windows, and Macintosh user interface. It is entrusted as an application that provides functionality as a data mining engine.

The graphical interface provided by RapidMiner application contrasts slightly to the user interface of other commercial data mining applications like IBM SPSS modeler, SAS enterprise and Statistical data miner.

Overview

RapidMiner operates on a client-server paradigm, with the server available on social, distinct cloud interfaces or premises. As a result of its all-encompassing programming, RapidMiner is considered optimal for data mining primary functions like data cleansing, filtering and clustering. It recognizes the types of characteristics in a dataset automatically and each attribute serves a specific purpose. It deploys similar techniques to alter and change, if any modifications are required, as script redevelopment will prolong the time duration.

RapidMiner aims to store datasets in memory for as long as possible. As a result, it will not discard the results of prior operations if memory is available. RapidMiner is identified as an application tool in the corporate market, that has the essential depth in its capabilities to deliver results, close to the business market value.

The data analysis techniques in RapidMiner follow a sequential approach as listed below.

Identifying the issue: RapidMiner provides users with a wide array of data sources, this creates an opportunity to seek for a hidden pattern that can be advantageous to find as many types as possible of linked data sources, feasible for insight.

Creating data requirements: Data analytics addressing a specific problem need data sets from related areas. The determination of data sources can be from the domain specification, and datasets can be from problem definition.

Pre-processing data: Method of converting raw data into a preset data structure, before passing it through algorithms. Structured data will be proposed as an input to the data analytics process. Before being used by multiple nodes with Mappers and Reducers in Hadoop clusters, datasets must be prepared and moved to Hadoop distributed file system (HDFS).

Analyzing data: Data analytics application will be completed after the data is in usable condition. Data analytics apps are used in data mining concepts to choose critical knowledge from data to make better business decisions.

Visualizing data: The ability of analysts to evaluate massive amounts of data and make useful conclusions gives them a limited option to explain results. Data visualization is a technique for presenting the results of the data to Business users in a clear and presentable manner (Pynam, Spanadna and Srikanth, 2018).

RapidMiner has several products that can be utilized to execute different tasks. These products are

RapidMiner Studio

RapidMiner Auto Model

RapidMiner Turbo Prep

RapidMiner Go

RapidMiner Server

RapidMiner Radoop

In this report we are emphasizing on the product, RapidMiner Studio educational. RapidMiner Studio combines the structured with the unstructured data, the combination obtained is used for predictive analysis. It allows appropriate and accurate model performance means. The guidelines set in the encryption of the application are paramount, as it provides a gap between model training and model built-in application, ensuring no leak of information (Savaram, 2022).

Market Trend

Companies have huge set of data to be analyzed and evaluated to produce valuable information helping in identifying many useful business targets such as customer segmentation, sales management, and target marketing. Transforming data into useful information need data mining (DM) tools and software; many of those software has been introduced to the market such as RapidMiner, WEKA, Orange, KNIME, SAS, R tool, Tableau public, OpenRefine and Solver software. Analyzers and companies can choose to use one of the DM software or can combined two or more data mining software to benefit from its different strength (Data Mining Tools for Better Data Analysis, 2022). Though, companies can select the best option for analyzing its own data easily and more efficiently.

Monty Widenius, main author of the original version of the open source MySQL database (Blattberg, 2013); said ‘RapidMiner has rapidly become one of the leaders in the predictive analytics space' (TechCrunch Is Part of the Yahoo Family of Brands, 2013). On 2011, in Rexer Analytics Data Miner Survey, RapidMiner received one of the strongest satisfaction ratings (Dwivedi, Kasliwal, Soni, 2016).

KDnuggest website, ‘a site of analytics, big data, data mining, data science and machine learning founded by Gregory Piatetsky-Shapiro’, used to rank data mining software annually in Annual Analytics, Data Mining, Data Science Software Poll (n.d.). A question was asked annually by a website was: "What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?". The number of voters greatly increased from 534 in 2007 up to more than 3,000 voters in 2014. Most popular platforms were included in the options and the options usually have to be less than 90 tools. The software was divided into commercial software and free software. Figure 1 of Appendix A shows that Yale (now RapidMiner) software was ranked in the first place when compared to other free platforms, 103 out of 534 voters were using Yale with other DM software and 70 voters were using it alone. The number of voters were very close to the top commercial software, SPSS Clementine, 189 users were using this software alone (KDnuggets, 2007). RapidMiner was the top used DM software until 2014. In 2015, RapidMiner software was ranked the second level after R software (Piatetsky, 2015). Figure 2 of Appendix A shows the top data mining tools between 2015-2017 where RapidMiner appears to be in the 4th place.

In 2021, RapidMiner was one of the top DM programs used by big companies and businesses such as BMW, Hewlett Packard Enterprise, EZCater, Sanofi. It was ranked in the fourth place of other software which are R and Python, Microsoft Excel and Tableau respectively. However, this does not negate that other big companies uses other competitor platforms, for example, Google and Firefox use R, YouTube, Netflix and Facebook use Python which were the top programming platform in 2021 (Kappagantula, 2021).

A study comparing four analytical tool software, RapidMiner, Weka, R tool and KNIME concluded that KNIME followed by Weka are recommended to be used by beginners rather than highly skilled individuals because of their build-in features requiring no programming knowledge. On the other hand, RapidMiner program is more suitable for experts and need more programming experiences despite that it has easy use graphical tools. Moreover, RapidMiner platform, unlike other three platforms, could be easily implemented in different types of systems because of its independency of language limitation (Comprehensive Study of Data Analytics Tools (RapidMiner, Weka, R Tool, Knime), 2016). In Finances Online website compared between MATLAB and RapidMiner software in different aspects user satisfaction was 100% for RapidMiner compared to 95% for MATLAB. Among many of DM software, RapidMiner Studio could be downloaded easily from the internet for 30 days free use and RapidMiner Radoop can be fully free for one user (Top 10 Data Science Tools in 2022 to Eliminate Programming, 2022). Python and R software are classified as “Language” program however RapidMiner is “ Data science Tool” (BaseDash, 2022). While Power BI AND Tableau are easy to start with platforms that allow users to build-in visualizations when compared to RapidMiner (RapidMiner vs Tableau, 2020).

Advantages and Limitations of RapidMiner

Advantages

Below are some advantages of RapidMiner which make it better from other open-source analytical platform.

RapidMiner

Open source, predictive analytics platform might be installed easily in various OS.

The outcome is easily perceived and understood, as RapidMiner use Java Programming.

Powerful in mining data, not requiring technical users, as operators ready for use, “It is memory-based system”.

Use of massive data generator operators to manage big data with acceptable speed.

Used in Business Process Management, business intelligence, training, research, education, rapid prototyping Clinical Data, Machine Learning, classification, and clustering.