Every single second on the web, a huge amount of data is generated and collected. This data could be extremely useful if processed in the right way. But how is collected data made useful?
Data is made useful by extracting desired information from it. This extraction of information, by applying algorithms and visualizations is known as data mining.
And much to your amazement, there are data mining tools, with the help of which, you can extract useful information from the collected data.
Data Mining Stages
Data mining is generally the collective term for the methods used in segmenting, clustering, analyzing and inferring results from data, on the basis of various dimensions, factors, cases or perspectives.
The stages of data mining involve the following:
- Target data
- Pre-processed data
- Pattern Identification or Models Creation
- Knowledgeable deductions
Data Mining Tools
As said earlier, there are many tools for data mining. Each tool has its own merits. No toll is better than other as most of the data mining tools are still developing. However, the usability of the data mining tool is dependent on the requirement of the user.
With the help of data mining tools, one can:
- draw insights and useful projections
- implement standard algorithms from scratch
- finding unknown, hidden patterns
- classify and group data
- identify the relationship and dependencies
List of Data Mining Tools
The data mining tools are very important in enhancing the efficiency of data scientists, and even students who are working on research projects. Given below is data mining tools list, with the help of which you can enhance your predictive and descriptive analysis.
Having talked about Python, let us come to the king of all available free data mining tools, R. It has everything for data manipulation, data visualization, and data analysis – that means you can perform three different tasks in one single place. In the world of data science, R is better known as “Excel for a new generation.” With that, you must have understood the simplicity and utility of R.
The second most widely used data mining tool after R is undoubtedly RapidMiner. It is ideal for startups and smart businesses that require their mobile applications or chatbots to be powered by machine learning, text mining and predictive analysis, for enhancing the user experience.
Orange is an open source software for machine learning and data visualization. It offers visual programming, interactive data visualization, opt-in usage tracking, easy construction of workflows, and much more. Thus, Orange becomes helpful for both novice and experts, as it makes data mining fruitful and fun.
A rattle is another widely used tool for data mining, more because it is also open source. It offers a graphical user interface and is totally based on the language R. The tool is capable of presenting the data in the form of models graphically. Also, it shows the performance of those models in various case scenarios. The tool is ideal for statistical and visual summaries of data.
TANAGRA is another free open source data mining software, which is proven for uses like database management, data analysis, statistical learning, and machine learning. The Tanagra software has been designed such that it can process both real-world data and synthetic data (assumed data) easily. Besides, it also has kept usability at high priority. Thus, it has a library of study materials and e-books that make it easy to learn and use this data mining tool. You may also check it out.
You may call Python as the celebrated figure of the data mining world. It is an open source language, which you can download and install on your computer system, and begin building data sets after a tutorial. It is easy to use that many people don’t find writing complex codes difficult. You will fall for the syntax and use case data visualizations in Python. Just ensure you have good hands on programming concepts like variables, data types, functions, conditionals and loops.
The data to be processed with machine learning algorithms are increasing in size, especially when we talk about unstructured data. Under such scenarios, the tools for data mining and data analysis are a boon. Hope this helps you to learn data science applications better.
Data Mining Applications
With the increased use of the internet in our lives, we seriously cannot pretend to be unknown to the huge amount of data generation and collection.
Companies with a strong inclination towards customer experience and customer satisfaction, make the best use of this data.
The primary step for extracting the best information from the collected data is data mining. Almost all business sectors - financial, retail, communications, marketing, etc. use data mining tools for drilling down their user's intent.
List of Data Mining Applications
Data mining can be used for the determination of pricing structure, product positioning, sales target, corporate profits, discount offers, new additions, improvements in process, etc.
Let us see data mining applications in 8 different industries.
Many improvements can be made in the health systems with the help of data mining tools. With the analysis of past data, health care management can be improved for furnishing reduced medical costs. Data mining approaches like multi-dimensional databases, machine learning, soft computing, data visualization, and statistics, are widely used features in healthcare research.
Not many of us will be able to digest the fact that even educational data is put for research. Educational Data Mining concerns with the extraction, segmentation, and analysis of data resulting from educational institutes, such as the age of students, courses enrolled in, marks scored in, etc. By using the data, institutes can improve their results, change course pattern, seek more admissions, implement new technologies for teaching, and so on.
The entire process of production of a certain commodity from raw materials is a complex and time-consuming event. There are various stages involved in manufacturing. With the help of data, the shortcomings in the process such as delays can be removed. The major application of data mining in manufacturing is in the design of system and removal of flaws from it.
As banking and all other payment applications go online, much user data is collected by financial institutions. This data can be instrumental in deriving user’s expense intent, savings limits, etc. and be made useful for marketing purposes. Also, this data can be used by financial institutes themselves for pitching new offers and services to customers.
The purchasing history of the user can be used to draw patterns of user’s buying interests and its variations over a period of time. The stores can rotate their products from time to time as per the data, so as to increase the sales. Also, the retail stores can launch various offers and discounts based on this data. Altogether, this data mining implementation will help in conversion rate optimization.
6.Human Resource Management
Strange, but true. People with similar kinds of traits such as faith, age group, gender, qualification etc. have nearly similar behavior, habits, practices, virtues, and interests. Companies can analyze their previous employees’ data for predicting the efficiency of new employees or people who are yet to be hired. Data mining applications can be very helpful in human resource management.
This is another application of predicting people’s behavior by comparing it with data from other people’s behavior in similar conditions. Data mining is helpful when criminal investigation requires judging the words of a convict, to find out if it’s lies or truth. Also, when investigators need to connect the dots to identify the crime scene. The high volume of crime datasets and the complexity of relationships between them makes data mining more necessary in these cases.
With the help of data, users can be fed with tailored advertisements across the internet. In simple words, a person who purchased a pair of sports shoes from Amazon is likely to buy similar sports products, if an offer is made to him. And the work of advertising is made easy by targeting such users based on the product or service. If advertisements bank upon the interest of the user, the ROI can be optimized to a great extent.
These are 8 useful applications of data mining, but the world of data science is not limited to this. It is emerging at a fast pace and paving the way into more sectors. Soon, we will also see data mining applications in untouched and unexpected sections. Let us wait for the future.