Tools in Data Mining - Naukri Code 360

Introduction

The practice of extracting potentially valuable patterns from large data sets is known as data mining. It is multidisciplinary expertise that combines machine learning, statistics, and artificial intelligence to extract data and assess the likelihood of future events. Data mining insights are used for marketing, fraud detection, scientific research, etc.

Data mining identifies previously unknown but valid relationships among concealed, unanticipated, and previously unknown data. Knowledge Discovery in Data (KDD), knowledge extraction, data/pattern analysis, information harvesting, and other terms are used to describe data mining. It basically converts raw data into useful information.

Without further ado, let us jump into the implementation of data mining and various data mining tools.

Process in Data Mining

The entire data mining process cannot be finished in a single step. In other words, extracting the essential information from enormous amounts of data is not that simple. It's a far more complicated procedure than we imagine, comprising several steps.

Data preprocessing and mining are the two elements of the data mining process. Data cleaning, integration, reduction, and transformation are all part of data preprocessing, while data mining, pattern evaluation, and knowledge representation are all part of data mining. All these steps are followed in the sequence mentioned.

Implementation of Data Mining

The implementation of data mining can be briefly described as follows:

Business understanding: This step establishes the business and data-mining objectives.
Data understanding: During this stage, a sanity check is conducted on the data to see if it is suitable for the data mining aims.
Data preparation: Data is prepared for production in this phase. The data preparation procedure takes up roughly 90% of the project's time. Data from various sources should be chosen, cleansed, processed, formatted, anonymised, and built.
Data transformation: Data transformation processes would aid in the mining process's success.
Modelling: Mathematical models are utilised to determine data trends at this phase.
Evaluation: In this stage, the identified patterns are compared to the company's goals.
Deployment: In the deployment phase, you take your data mining discoveries and integrate them into your regular company activities.

Data Mining Tools

Data mining tools are software programmes that assist in the creation and testing of data models by designing and executing data mining processes. It's usually a framework with a set of programs to assist in designing and testing a data model, such as R studio or Tableau.

There are numerous open-source and proprietary tools available, each with differing levels of sophistication. Each tool aids in the implementation of a data mining strategy at its core, but the distinction resides in the level of sophistication required by the software's customer. There are instruments that excel in a particular subject, such as the financial or scientific fields.

Let's look at some of the most popular options on the market.

Oracle Data Mining

Oracle Advanced Analytics Database is part of the Oracle Enterprise Edition. Oracle, the world leader in database software, has combined its database technologies with analytical tools to deliver clients Oracle Advanced Analytics Database. It includes classification, regression, prediction, anomaly detection, and other data mining algorithms. This is proprietary software maintained by the Oracle technical team to assist your company in establishing a comprehensive data mining infrastructure at the corporate level.

The algorithms are directly integrated with the Oracle database kernel and function natively on data stored in its own database, removing the requirement for data extraction into standalone analytics servers. The Oracle Data Miner is a set of graphical user interface tools that guide users through the process of building, testing and implementing data models.

SAS Data Mining

SAS is the abbreviation for Statistical Analysis System. It is a SAS Institute tool designed for analytics and data management. SAS can mine data, alter it, manage data from various sources, and do statistical analysis. It has a graphical user interface (GUI) for non-technical users.

SAS data miners allow users to evaluate large amounts of data and deliver reliable information for quick decision-making. SAS features a highly scalable distributed memory processing architecture. It can be used for data mining, optimisation, or text mining.

Kaggle

Kaggle is the world's largest data scientist and machine learning community. Kaggle began as a machine learning competition site but has since evolved into a public cloud-based data science platform. Kaggle is a platform that aids in finding the solution to challenging problems, recruiting strong teams, and the enhancement of data science's capacity. Kaggle now has the code and data you'll need for your data science projects. You can access more than 50k public datasets and 400k public notebooks to boost your data mining efforts. Kaggle's large online community serves as a safety net for implementation-related matters.

Orange

Orange is a data science and machine learning package that uses python scripting and visual programming to provide interactive data analysis and component-based data mining system construction.

It includes a huge number of pre-built machine learning algorithms and text mining add-ons. For bioinformaticians and molecular biologists, it also includes additional features.

Most Python-based data mining and machine learning tools don't have as much functionality as Orange. It is a software that has been actively developed and used for over 15 years. In addition, Orange provides a visual programming platform with a graphical user interface (GUI) for interactive data visualisation.

RapidMiner

Rapid Miner is one of the most widely used predictive analysis tools developed by the Rapid Miner corporation. It was created using the JAVA programming language. It includes text mining, deep learning, machine learning, and a predictive analysis environment.

Company applications, commercial applications, research, education, training, application development, and machine learning are all possible applications for the instrument.

Rapid Miner can host the server on-premises or in a public or private cloud environment. It is based on a client/server model. A rapid miner has template-based frameworks that allow quick delivery with minimal errors.

Non-programmers may design predictive processes for specific use cases like fraud detection and customer attrition using its drag-and-drop interface and pre-built models. Meanwhile, programmers may personalise their data mining using RapidMiner's R and Python extensions.

Last but not least, this platform features a vast and active user community that is always willing to assist.

Rattle

Togaware's Rattle GUI is an open-source and free software package that provides a graphical user interface for data mining using the R Programming Language. Rattle exposes the power of R through a graphical user interface, providing significant data mining functionality. Rattle can also be used as a tool for learning the R. The Log Code tab is an option that replicates the R code for any activity performed in the GUI and may be copied and pasted. Rattle can be used to perform statistical analysis or create models. Rattle allows you to divide your dataset with three sections: training, validation, and testing. The dataset is viewable and editable.

Python

Python is a free and open-source programming language with a relatively short learning curve. Python is a terrific tool for enterprises who want their software to be custom created to their specifications, thanks to its capacity as a general-purpose language and a vast library of packages that assist in establishing a system for creating data models from scratch.

You won't get the fancy features that proprietary software provides with Python. Still, anyone can pick up and construct their own environment using their own graphical interfaces. Python is also supported by a robust online community of package authors who guarantee that the packages available are stable and secure.

Python's excellent on-the-fly visualisation features are among its most prominent features in this sector.

KNIME

KNIME(Konstanz Information Miner) is a data mining and machine learning platform that is open-source and free. Its user-friendly interface lets you design entire data science workflows, from modelling to production. Various pre-built components also allow for quick modelling without having to write a single word of code.

KNIME is a versatile and scalable platform for processing complex data and using advanced algorithms thanks to a range of powerful extensions and interfaces.

Data scientists can use KNIME to build analytics and Business Intelligence apps and services. Credit scoring, fraud detection, and credit risk assessment, for example, are all common use cases in the financial business.

Teradata

A cloud data analytics platform sells a full suite of enterprise-scale solutions that includes no-code tools. You don't need to be a coder to code complex machine learning algorithms using Vantage Analyst. It is a simple GUI-based solution that the entire enterprise can quickly adopt.

Teradata is used to gain an understanding of company data like sales, product placement, and consumer preferences, among other things. It can also distinguish between "hot" and "cold" data, putting less often utilised data in a slower storage portion.

Teradata has a 'share nothing' architecture, with each server node having its own memory and processing power.

Apache Mahout

Apache Mahout is an open-source framework for building scalable machine learning applications. Its purpose is to assist data scientists and researchers with the implementation of their own algorithms.

This system, which is written in JavaScript and runs on Apache Hadoop, focuses on three primary areas: recommender engines, clustering, and classification. It's ideal for large-scale, sophisticated data mining operations involving massive amounts of data. Some of the most well-known web companies, such as LinkedIn and Yahoo, use it.

Under the Apache licence, Apache Mahout is free to use and is backed by a vast user community.

Weka

Weka(Waikato Environment for Knowledge Analysis) is an open-source machine learning software that includes a large number of data mining methods. It was written in JavaScript and produced by the University of Waikato in New Zealand.

It has a graphical interface that makes it simple to use and supports many data mining tasks such as preprocessing, classification, regression, clustering, and visualisation. Weka has built-in machine learning algorithms for each of these tasks, allowing you to quickly test your ideas and deploy models without writing any code.

Weka was created with the intention of analysing data in the agricultural industry. It is now utilised mainly by researchers and industrial scientists, as well as educators. It is free to download and use under the GNU General Public License terms.

H2O

H2O is an open-source machine learning platform that aspires to make AI technology accessible to everyone. It supports the most common machine learning methods. It has Auto ML functionalities to assist users in quickly and easily building and deploying machine learning models, even if they are not experts.

H2O uses distributed in-memory computing and can be integrated via an API, which is available in all major programming languages, making it perfect for analysing large datasets.

Sisense

When it comes to reporting within the organisation, Sisense is the most useful and well-suited BI software. It has a fantastic ability to handle and analyse data for both small and large businesses. It is not open-source software; instead, it is licenced software, and we must buy a licence to use it.

It enables users to combine data from many sources to create a single repository and then enhance the data to create rich reports that can be shared across departments for reporting.

Sisense generates visually appealing reports. It is created specifically for non-technical users. It has a drag-and-drop feature along with widgets. Depending on the goal of an organisation, several widgets can be selected to generate reports in the shape of pie charts, line charts, bar graphs, and so on. Reports can be dug down even more by just clicking to see more facts and statistics.

Frequently Asked Questions

What are the various types of data on which data mining can be performed?

The various types of data on which data mining can be performed are as follows:

→ relational databases

→ data warehouses

→ text databases

→ text mining and web mining

→ multimedia and streaming databases

→ heterogeneous and legacy databases

→ transactional and spatial databases

→ object-oriented and object-relational databases

Why is data mining important?

Data mining is an integral part of every organisation's analytics programme. The data generated can be used in BI and advanced analytics programmes that analyse historical data and real-time analytics systems that look at data as it's being created or collected. Effective data mining benefits several elements of business strategy development and operations management.

State the applications of data mining.

Here are some examples of how companies in various industries employ data mining as part of their analytics applications:

→ Retail: Customers' data and internet clickstream records are mined by online retailers to assist their target marketing campaigns, advertising, and promotional offers to specific customers. The recommendation engines that propose potential purchases to website users and inventory and supply chain management activities are all powered by data mining and predictive modelling.

→ Financial services: Data mining technologies are used by banks and credit card companies to create financial risk models, detect fraudulent activities and assess loan and credit applications. Data mining is also essential for marketing and discovering potential upsell chances with current customers.

→ Entertainment: Data mining is used by streaming services to assess what consumers are watching or listening to and to provide customised suggestions based on their preferences.

→ Healthcare: Doctors use data mining to diagnose medical disorders, treat patients, and analyse the results of X-rays and other medical imaging. Data mining, machine learning, and other forms of analytics are also used extensively in medical research.

Name some disadvantages of data mining.

Data mining has some drawbacks.

→ There's a danger that businesses will sell their customers' vital information to other companies for a profit.

→ Many data mining analytics software programmes are difficult to use and require extensive training.

→ Because of the various algorithms used in their development, different data mining tools work in different ways. As a result, picking the right data mining tool is complex.

→ Because data mining techniques are inaccurate, they can have catastrophic effects in certain instances.