DM-15-

Please read and study the entire online book, “Data Mining Tutorial- Introduction to Data Mining (Complete Guide).” This is a wonderful look at the major topics and tools of modern data mining studies. You will be doing much of what is discussed in the book in this class and we will continue much of the classic data science components of this book in the next class in this sequence (IS 5213 – Data Science and Big Data). You are to discuss your thoughts about this reading. Please discuss what experience you have with data mining and data visualization, what you would like to learn from these topics, and where you find your future in the field of Data Science.

Submit your answers in 1-2 page word document to this assignment. 

Running Head: DATA MINING 1

DATA MINING 4

Data Mining

Institutional Affiliation

Student Name

Date

Data Mining

Data mining is a collection of methods used on large and complex databases. Data mining is a technique for eliminating randomness from data by revealing hidden patterns. Data mining theories, methodologies, and tools are used to identify data patterns. Because data mining is a vast and complicated field of study, it is an important study topic. I’m interested in learning about data mining methods, foundations, and architectures. I’d also like to elaborate on the knowledge discovery process. Data mining was known as data dredging and data fishing in the 1960s (Roiger, 2022). Product development makes use of data mining and visualization techniques. These methods enable an organization to browse and view its stored data in real time. Data mining and visualization software includes complex algorithms, powerful multiprocessors, and massive data collection and storage tools.

Data mining is required because there is a large amount of data available that must be transformed into knowledge and information. Software, for example, market analysis, makes use of knowledge and information. Data mining collects a wide range of data types in massive quantities. Data collected includes personal and medical information, scientific data, surveillance images and videos, games, business transactions, and digital media (Slater et al., 2021). Emails, text reports, memos, CAD engineering data, and data from virtual worlds are all examples of data. Data mining techniques can be used for a variety of purposes, including automated behavior and trend prediction, as well as the discovery of previously unknown data patterns.

A variety of techniques can be used in data mining. Mutation, the current form of natural selection known as genetic algorithms, and a combination of optimization techniques are among these methods. The nearest neighbor method, artificial neural networks, rule induction, and decision trees are some other data mining techniques (Slater et al., 2021). Knowledge discovery in a database refers to the process of extracting implicit knowledge from stored data. The knowledge discovery process consists of several steps. Some of these steps are data cleaning, integration, selection, transformation, and data mining. The final ones involve evaluating discovered patterns and then presenting the knowledge.

Data mining has both benefits and drawbacks. High implementation costs, potential information misuse, inaccurate data, massive amounts of data, and concerns about user security and privacy are all disadvantages of data mining. The advantages may include assisting management in decision-making, detecting fraud, lowering costs and increasing revenue, forecasting future customer habits and trends, and providing target market analysis (Roiger, 2022). Data mining assists online advertisers in placing relevant and useful advertisements. Data mining can be used by financial and banking institutions to identify potential future defaulters. This identification is accomplished through the examination of transactional data, data patterns, and user behavior.

References

Roiger, R. J. (2022). Data mining: a tutorial-based primer. Chapman and Hall/CRC.

Slater, S., Joksimović, S., Kovanovic, V., Baker, R. S., & Gasevic, D. (2021). Tools for educational data mining: A review. Journal of Educational and Behavioral Statistics, 42(1), 85-106.

Outline: DATA MINING

Data Mining

Data mining is a collection of techniques used to analyze complex and large databases. Data mining aids in the discovery of hidden patterns in data and the elimination of randomness. To discover data patterns, data mining theories, methodologies, and tools are used.

The availability of large amounts of data, as well as the need to convert this data into useful knowledge and information, drives the demand for data mining. Knowledge and information are used in software applications such as market analysis.

Data mining can make use of a variety of techniques. Genetic algorithms, which are currently used in natural selection, mutation, and a combination of optimization methods are among these techniques.

References

Roiger, R. J. (2021). Data mining: a tutorial-based primer. Chapman and Hall/CRC.

Running Head: DATA MINING 1

DATA MINING 4

Data Mining

Institutional Affiliation

Student Name

Date

Data Mining

Data mining is a collection of methods used on large and complex databases. Data mining is a technique for eliminating randomness from data by revealing hidden patterns. Data mining theories, methodologies, and tools are used to identify data patterns. Because data mining is a vast and complicated field of study, it is an important study topic. I’m interested in learning about data mining methods, foundations, and architectures. I’d also like to elaborate on the knowledge discovery process. Data mining was known as data dredging and data fishing in the 1960s (Roiger, 2022). Product development makes use of data mining and visualization techniques. These methods enable an organization to browse and view its stored data in real time. Data mining and visualization software includes complex algorithms, powerful multiprocessors, and massive data collection and storage tools.

Data mining is required because there is a large amount of data available that must be transformed into knowledge and information. Software, for example, market analysis, makes use of knowledge and information. Data mining collects a wide range of data types in massive quantities. Data collected includes personal and medical information, scientific data, surveillance images and videos, games, business transactions, and digital media (Slater et al., 2021). Emails, text reports, memos, CAD engineering data, and data from virtual worlds are all examples of data. Data mining techniques can be used for a variety of purposes, including automated behavior and trend prediction, as well as the discovery of previously unknown data patterns.

A variety of techniques can be used in data mining. Mutation, the current form of natural selection known as genetic algorithms, and a combination of optimization techniques are among these methods. The nearest neighbor method, artificial neural networks, rule induction, and decision trees are some other data mining techniques (Slater et al., 2021). Knowledge discovery in a database refers to the process of extracting implicit knowledge from stored data. The knowledge discovery process consists of several steps. Some of these steps are data cleaning, integration, selection, transformation, and data mining. The final ones involve evaluating discovered patterns and then presenting the knowledge.

Data mining has both benefits and drawbacks. High implementation costs, potential information misuse, inaccurate data, massive amounts of data, and concerns about user security and privacy are all disadvantages of data mining. The advantages may include assisting management in decision-making, detecting fraud, lowering costs and increasing revenue, forecasting future customer habits and trends, and providing target market analysis (Roiger, 2022). Data mining assists online advertisers in placing relevant and useful advertisements. Data mining can be used by financial and banking institutions to identify potential future defaulters. This identification is accomplished through the examination of transactional data, data patterns, and user behavior.

References

Roiger, R. J. (2022). Data mining: a tutorial-based primer. Chapman and Hall/CRC.

Slater, S., Joksimović, S., Kovanovic, V., Baker, R. S., & Gasevic, D. (2021). Tools for educational data mining: A review. Journal of Educational and Behavioral Statistics, 42(1), 85-106.

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