2. Introduction
Today's Internet is an important place for exchanging data such as text,
images, audio, and video, and for sharing information, preferably in digital
form. Using the Internet leads to accessing a huge amount of data. The
data may be unstructured data, structured data, and semi-structured data.
So we store and process such a huge amount of data of enormous
complexity [2].
Therefore, it leads to the use of highly efficient and advanced tools and
techniques to analyze and process this data. Analyzing and processing
data allows understanding of useful information and knowledge about data.
The term “data mining” appeared in the 1990s [3]. So the investigation of
knowledge in data is nothing but data mining [4]. Mining is important
because it gives learning about the diverse directions of life in the data [5]. 2
3. Introduction
Data mining is the process of discovering meaningful correlations, patterns,
and trends by transforming a large amount of data store into warehouses,
using pattern recognition techniques as well as statistical and mathematical
techniques [3]. We have a large amount of data available but no knowledge
about it. So data mining lends a way to experience knowledge from data.
Data mining refers to filtering, sorting, and categorizing data from larger
data sets to reveal subtle patterns and relationships, which helps
organizations identify and solve complex business problems through data
analysis. Data mining software tools and techniques allow organizations to
predict future market trends and make critical business decisions at critical
times[6].
3
5. “
The main objective of the research is to provide an overview of the 10 best
data mining tools - whether open source, proprietary, data integration, ease
of use, or the programming language used. The preference of the tools was
chosen based on 10 sites as follows:
5
Background
•SPICeworks[9]
•Javapoint[8]
•UPWORK[6]
•Monkeylearn[10]
•HEVO[7]
•Software Testing Help[15]
•SELECTHUB[11]
•CAREERFOUNDRY[14]
•IMAGINARY CLOUD[13]
•GURU99[12]
6. “
Ten data mining tools have been nominated based on the previous sites,
and they are in the following order:
6
Background
6.Orange
7. Oracle Data Mining (ODB)
8. Rattle
9.Apach Machout
10.Teradata
1.RapidMiner
2.SAS Enterprise Mining
3. Knime
4.IBM SPSS Modeler
5. Weka
7. Criteria for Selecting Data Mining
Tools
7
Data integration
Security
Open source or proprietary
programming language
functions
and methodologies
Ease of use
1
2
3
4
5
6
9. Rapid Miner is an open source data mining tool with seamless integration with
both R and Python. This open source is written in Java and can be integrated with
WEKA and R-tool.
A data science software platform that provides an integrated environment for the
various phases of data modeling including data preparation, data cleansing,
exploratory data analysis, visualization, and more. The technologies that the
software helps with are machine learning, deep learning, text mining, and
predictive analytics. Easy-to-use tools and a graphical user interface take you
through the modeling process.
The tool can be used for a wide range of applications, including corporate and
commercial applications, research, education and training, application
development, and machine learning. It has a client/server model as its base
9
12. SAS stands for Statistical Analysis System. It is a product of the SAS institute that was
created to manage analytics and data. SAS can extract and alter data, manage
information from different sources, analyze statistics, and allow users to analyze big
data and provide accurate insight for timely decision-making purposes. SAS has a
highly scalable distributed memory processing architecture. It is suitable for data
mining, optimization, and text mining purposes. Its data mining features include the
ability to perform exploratory and preparatory analyzes of vital data, all while producing
accurate reports or summaries of your findings. SAS Enterprise Mining is well suited
for companies large and small that intend to implement fraud detection applications or
applications that enhance targeted customer response rates through marketing
campaigns. SAS Enterprise Miner has benefits that you may not get from open source
data mining tools, such as secure cloud integration and code logging (which ensures
that your code is clean and free of potentially expensive bugs). On the downside, its
GUI is functional but a bit outdated, which for an enterprise tool might seem a bit below
15. KNIME (short for Konstanz Information Miner) is another open source data
integration and data mining tool. It incorporates machine learning and data
mining mechanisms. KNIME is used for a full range of data mining
activities including classification, regression, and dimensionality reduction
(simplification of complex data while retaining the meaningful properties of
the original dataset). You can also apply other machine learning
algorithms such as decision tree, logistic regression, and k-means
clustering. Other useful functions of KNIME range from data cleaning to
analysis and reporting, which means that it is much more than just a data
mining tool. Finally, it also integrates with Python and R (as well as other
coded packages) though KNIME is implemented in Java, it also integrates
with Ruby, Python, and R. 15
18. SPSS is one of the most popular statistical software platforms. IBM SPSS Modeler
is known for its ability to better bridge the data mining process and visualize the
processed data. The tool allows importing large amounts of data from many
disparate sources to reveal hidden data patterns and trends. The basic version of
the tool works with spreadsheets and relational databases, while text analytics
features are available in the premium version. The tool helps organizations easily
leverage data assets and applications. One of the advantages of proprietary
software is its ability to meet the robust security and governance requirements of
an enterprise at the enterprise level. The advanced capabilities of the program
provide an extensive library of machine learning algorithms, statistical analysis
(descriptive, regression, clustering, etc.), text analysis, integration with big data,
and so on. Furthermore, SPPS allows the user to enhance SPSS Syntax with
Python and R using specialized extensions. 18
21. Also known as Waikato Environment is an open source machine learning
software developed at the University of Waikato in New Zealand. It is best
suited for data analysis and predictive modeling and contains a large set
of algorithms for data mining. It is written in JavaScript.
Weka has a graphical user interface that facilitates easy access to all of its
features. It is written in the Java programming language.
Weka supports major data mining tasks including data mining, processing,
visualization, regression etc. It operates on the assumption that the data is
available in the form of a flat file.
Weka can provide access to SQL databases through a database
connection and can process the data/results returned by the query.
21
24. Orange is a free and open source data science toolkit for developing,
testing and visualizing data mining workflows. , uses Python scripting and
visual programming that features interactive data analysis and
component-based compilation of data mining systems. Orange offers a
broader range of features than most other Python-based machine learning
and data mining tools. It is a program that has more than 15 years of
development and active use. Orange also offers a visual programming
platform with a GUI for interactive data visualization.
It is a component-based software, with a wealth of pre-built machine
learning algorithms and text extraction add-ons.
24
27. Oracle Data Mining is a component of Oracle Advanced Analytics that enables
data analysts to build and implement predictive models. It has many data mining
algorithms for tasks like classification, regression, deviation detection, prediction,
and more. With Oracle Data Mining, you can create models that help you predict
customer behavior, segment customer profiles, detect fraud, and determine the
best prospects to target. Developers can use the Java API to integrate these
models into business intelligence applications to help them discover new trends
and patterns.
This is software that is proprietary and supported by Oracle's technical team in
helping your business build a robust enterprise-wide data mining infrastructure.
27
30. Apache Mahout is an open source platform for building scalable
applications using machine learning. Its goal is to help data scientists or
researchers implement their own algorithms.
It is a project developed by the Apache Foundation that serves the primary
purpose of creating machine learning algorithms. It mainly focuses on data
aggregation, classification, and collaborative filtering.
It is written in Java and includes Java libraries to perform arithmetic
operations such as linear algebra and statistics. Mahout is constantly
growing because the algorithms implemented inside Apache Mahout are
constantly growing.
Mahout has the following main features: Extensible Programming
Environment, Pre-built Algorithms, Math Experimentation Environment, 30
33. Ratte is a GUI based data mining tool that uses the R stats programming
language. Rattle reveals the statistical power of R by providing great data
mining functionality. Although Rattle has a comprehensive and
sophisticated user interface, it has an inbuilt log code tab that generates
duplicate code for any activity happening in the GUI The data set
produced by Rattle can be viewed and edited. Rattle gives other facilities
to review the code, use it for several purposes, and extend the code
without any restrictions.
33
36. Teradata is an open, massively parallel processing platform for developing
large-scale data warehousing applications.
It is a suitable mining tool for organizations that rely on multi-cloud
deployment setups. Such frameworks can easily access databases, data
lakes, and even external SaaS applications for an enterprise. Moreover,
with no-code deployment features, it becomes more manageable to
develop and analyze business models to make informed decisions.
Teradata is open for deployment on any public cloud platform such as
AWS, Google, and Azure. Data miners can also deploy the tool on-
premises or private cloud.
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37.
38. Conclusion
In this research, I have understood the need
for data mining tools. In addition, I have
explored the most popular and powerful data
mining tools.
Data mining needs to extract complex data
from a variety of data sources such as
databases, customer relationship
management, and project management tools
.as mentioned earlier, most data mining tools
are based on two major programming
languages: R and Python. Each of these
languages provides a complete set of
packages and libraries involved for data
mining and data science in general. Despite
the dominance of these programming
languages, integrated statistical solutions
(such as SAS and SPSS) are still heavily
38
![Introduction
Today's Internet is an important place for exchanging data such as text,
images, audio, and video, and for sharing information, preferably in digital
form. Using the Internet leads to accessing a huge amount of data. The
data may be unstructured data, structured data, and semi-structured data.
So we store and process such a huge amount of data of enormous
complexity [2].
Therefore, it leads to the use of highly efficient and advanced tools and
techniques to analyze and process this data. Analyzing and processing
data allows understanding of useful information and knowledge about data.
The term “data mining” appeared in the 1990s [3]. So the investigation of
knowledge in data is nothing but data mining [4]. Mining is important
because it gives learning about the diverse directions of life in the data [5]. 2](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)