After reviewing the case study this week by Krizanic (2020), answer the following questions in essay format.

1. What is the definition of data mining that the author mentions?  How is this different from our current understanding of data mining?
2. What is the premise of the use case and findings?
3. What type of tools are used in the data mining aspect of the use case and how are they used?
4. Were the tools used appropriately for the use case?  Why or why not?

 

Definition of data mining

The process of automatically discovering valuable data from huge data repositories is referred to as data mining. Data mining techniques are used to sort the data into patterns that may be unknown, and those patterns can be used to predict what kind of results or future observations one should be expecting. Not all actions carried on data stored in databases can be related to data mining or can be said to be actions of data mining because these simple actions are just traditional computer science techniques to help sort data from the repositories. Data mining techniques are used in improving the traditional techniques to improve the results obtained in data analysis. Differently, people understand data mining as analyzing huge information from databases or repositories to determine the patterns and trends of the information. Data mining techniques include clustering, data cleaning, preparation, prediction, classification, tracking patterns, associations, regression, and outlier detection (Atluri & Karpatne, 2018). Each of these techniques has its weakness and strength, but they improve the outcomes of data mining when used together.

The premise of use case and findings

Data mining is seen as the transitional process between knowledge discovery in databases, and it has popped up as an interesting field in academics within computer science. Data mining focuses on all features of knowledge discovery in databases, including postprocessing, mining, and data processing. In the 1980s, some workshops brought up researchers from diverse fields to talk about the problems faced when using computational techniques to analyze data to get knowledge and actional information from repositories. Within a short period, many organizations and academic researchers brought success in the field and grew tremendously. The researcher brought up ideas such as estimation, sampling, hypothesis testing, modeling techniques, artificial intelligence, search algorithms, and machine learning to ensure the results obtained in data mining are valuable. Adopted ideas in data mining include optimization, signal processing, information theory, visualization, and information retrieval. With the growing technology, massive databases are being created, and that’s why people need potent data mining tools.

Data mining tools

Data mining tools are sued to transform data into more refined information from patterns or large data sets. These techniques utilize given algorithms, database systems, statical analysis, and artificial intelligence from diverse perspectives. Data mining tools allow a researcher to perform more than one analytic technique to given information to come up with refined data. Firstly, the orange data mining tool is a software-based universal tool for data mining and machine learning. The widgets of orange display data in tables and allow the user to select the displayed feature of user data; the widgets are also used in data reading and comparing learning algorithms (Sarangam, 2018). When using orange, it formats the data into desired patterns and allows the user to make decisions quickly through quick analysis of data and comparing data. Data mining can be performed using python, which will enable widgets to run continuously without interruption.

Correctness in the usage of data mining tools

When well used, the orange data mining tool can give refined data that can be useful in predicting future happenings. By using machine learning, the device can predict outcomes when new data is fed to the tool. Available widgets, when well used, can be of help to the user because they help in the comparison of data using learning algorithms. The tool was used to format data into required formats that help researchers compile the information without challenges. There were no errors in the usage of this tool because of the efficiency in its functioning.

Outline

Data mining

1. Definition of data mining

• The process of automatically discovering useful information from massive data repositories is referred to as data mining.

2. The premise of use case and findings

• Data mining is seen as the transitional process between knowledge discovery in databases, and it has popped up as an interesting field in academics within computer science.

3. Data mining tools

• Data mining tools are sued to transform data into more refined information from patterns or large data sets.

4. Correctness in the usage of data mining tools

• When well used, the orange data mining tool can give refined data that can predict future happenings.

5. References