Application of data mining LEARNOVITA

Applications of Data Mining Free Guide Tutorial & REAL-TIME Examples

Last updated on 31st Oct 2022, Artciles, Blog

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Jamie (Senior Data Analyst )

Jamie provides in-depth presentations on various big data technologies and Data Analyst. She specializes in Docker, Hadoop, Microservices, Commvault, BI tools, SQL/SQL server/SSIS, DB2, Control-M, Oracle Workflow, Autosys, DB2, MSSQL Server, Oracle, and Microsoft Office, including Excel, Visio, and PowerPoint.

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    • In this article you will learn:
    • 1.What is Data Mining?
    • 2.Data Mining Steps.
    • 3.What Are the Benefits of Data Mining?
    • 4.Are There Any Drawbacks to Data Mining?
    • 5.Data Mining Applications.
    • 6.Conclusion.

What is Data Mining?

  • Typically, when someone talks about the “mining,” it involves people wearing helmets with a lamps attached to them, digging underground for natural resources. And while it could be a funny picturing guys in tunnels mining for the batches of zeroes and ones, that doesn’t exactly answer “what is a data mining.”
  • Data mining is a process of analyzing the enormous amounts of information and datasets, extracting (or “mining”) useful intelligence to help organizations solve the problems, predict trends, mitigate risks, and find new opportunities. Data mining is like the actual mining because, in both cases, the miners are sifting through the mountains of material to find the valuable resources and elements.
  • Data mining also includes the establishing relationships and finding patterns, anomalies, and correlations to tackle issues, creating actionable information in a process. Data mining is the wide-ranging and varied process that includes many various components, some of which are even confused for a data mining itself. For instance, statistics is the portion of the overall data mining process.

Data Mining Steps:

Understand Business: What is a company’s current situation, the project’s objectives, and what explains success

Understand a Data: Figure out what kind of a data is needed to solve issue, and then collect it from a proper sources.

Prepare a Data: Resolve a data quality problems like duplicate, missing, or corrupted data, then prepare data in a format suitable to resolve business problem.

Model Data: Employ algorithms to ascertain in data patterns. Data scientists create, test, and evaluate a model.

Evaluate Data: Decide whether and how effective results delivered by a specific model will help meet business goal or remedy the problem. Sometimes there’s an iterative phase for finding a best algorithm, especially if data scientists don’t get it quite right the first time. There may be a some data mining algorithms shopping around.

Deploy the Solution: Give a results of the project to the people in charge of a making decisions.

Data Mining

What Are Benefits of a Data Mining?

Since live and work in the data-centric world, it’s essential to get as many advantages as possible. Data mining offers us with the means of resolving problems and problems in this challenging information age. Data mining benefits are include:

  • It helps to the companies gather reliable information.
  • It’s an efficient, cost-effective solution compared to the other data applications.
  • It helps to businesses make profitable production and also operational adjustments.
  • Data mining uses the both new and legacy systems.
  • It helps to businesses make informed decisions.
  • It helps to detect credit risks and fraud.
  • It helps to data scientists easily analyze the enormous amounts of data quickly.
  • Data scientists can use information to detect fraud, build risk models, and improve product safety.
  • It helps to data scientists quickly initiate automated predictions of behaviors and trends and also discover hidden patterns.

Are There Any Drawbacks to a Data Mining?

Nothing’s perfect, including a data mining. These are the main issues in data mining:

  • Many data analytics tools are difficult and challenging to use. Data scientists need a right training to use the tools effectively.
  • Speaking of tools, different ones work with a varying types of data mining, depending on algorithms they employ. Thus, data analysts must be sure to choose a correct tools.
  • Data mining techniques are not infallible, so there’s always risk that the information isn’t entirely accurate. This obstacle is be especially relevant if there’s a lack of diversity in a dataset.
  • Companies can potentially sell customer data they have gleaned to the other businesses and organizations, raising privacy concerns.
  • Data mining needs large databases, making process hard to manage.

Data Mining Applications:

  • Financial Data Analysis.
  • Retail Industry.
  • Telecommunication Industry.
  • Biological Data Analysis.
  • Other Scientific Applications.
  • Intrusion Detection.

Financial Data Analysis:

The financial data in banking and financial industry is be generally reliable and of more quality which facilitates systematic data analysis and data mining. Some of typical cases are as follows −

  • Design and construction of a data warehouses for the multidimensional data analysis and data mining.
  • Loan payment prediction and also customer credit policy analysis.
  • Classification and clustering of the customers for a targeted marketing.
  • Detection of the money laundering and other financial crimes.

Retail Industry:

Data Mining has its great application in a Retail Industry because it collects the large amount of data from on sales, customer purchasing history, goods transportation, consumption and services. It is a natural that the quantity of data collected will continue to expand rapidly because of increasing ease, availability and popularity of web.Data mining in a retail industry helps in identifying customer buying patterns and trends that lead to be improved quality of customer service and good customer retention and satisfaction. Here is a list of examples of data mining in retail industry −

  • Design and Construction of a data warehouses based on benefits of data mining.
  • Multidimensional analysis of a sales, customers, products, time and region.
  • Analysis of an effectiveness of sales campaigns.
  • A Customer Retention.
  • Product recommendation and cross-referencing of items.
Data Mining Applications

Telecommunication Industry:

Today telecommunication industry is one of the most emerging industries providing a various services like fax, pager, cellular phone, internet messenger, images, e-mail, web data transmission, etc. Due to the development of a new computer and communication technologies, the telecommunication industry is a rapidly expanding. This is reason why data mining is become more important to help and understand the business.Data mining in telecommunication industry helps in identifying a telecommunication patterns, catch fraudulent activities, make better use of resource, and improve the quality of service. Here is list of examples for which data mining improves a telecommunication services −

  • Multidimensional Analysis of Telecommunication data.
  • Fraudulent pattern analysis.
  • Identification of unusual patterns.
  • Multidimensional association and sequential patterns analysis.
  • Mobile Telecommunication services.
  • Use of visualization tools in the telecommunication data analysis.

Biological Data Analysis:

In a recent times, we have seen a tremendous growth in a field of biology such as genomics, proteomics, functional Genomics and biomedical research. Biological data mining is a more important part of Bioinformatics. Following are aspects in which data mining contributes for the biological data analysis −

  • Semantic integration of the heterogeneous, distributed genomic and proteomic databases.
  • Alignment, indexing, similarity search and comparative analysis the multiple nucleotide sequences.
  • Discovery of a structural patterns and analysis of a genetic networks and protein pathways.
  • Association and path analysis.
  • Visualization tools in the genetic data analysis.

Other Scientific Applications:

The applications discussed above tend to handle the relatively small and homogeneous data sets for which statistical techniques are appropriate. Huge amount of data have been collected from the scientific domains such as geosciences, astronomy, etc. A large amount of data sets is being generated because of fast numerical simulations in different fields such as climate and ecosystem modeling, chemical engineering, fluid dynamics, etc. Following are applications of data mining in a field of Scientific Applications −

  • Data Warehouses and data preprocessing.
  • Graph-based mining.
  • Visualization and domain specific knowledge.

Intrusion Detection:

Intrusion refers to the any kind of action that threatens integrity, confidentiality, or availability of network resources. In this world of connectivity, security has become a major issue. With the increased usage of internet and availability of tools and tricks for intruding and attacking network prompted intrusion detection to become critical component of network administration. Here is a list of areas in which data mining technology may be applied for an intrusion detection −

  • Development of data mining algorithm for an intrusion detection.
  • Association and correlation analysis, aggregation to help a select and build discriminating attributes.
  • Analysis of a Stream data.
  • A Distributed data mining.
  • A Visualization and query tools.

Conclusion:

The use of data mining in an enrollment management is a fairly new development. Current data mining is a done primarily on simple numeric and categorical data. In future, data mining will include more difficult data types. In addition, for any model that has been designed, further refinement is a possible by examining other variables and their relationships. Research in a data mining will result in a new methods to find the most interesting characteristics in a data. As models are developed and also implemented, they can be used as a tool in an enrollment management.

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