Editorial Note on Data Mining

Sanika Swapna^*
*Correspondence: Sanika Swapna, Department of Biotechnology, Osmania University, Hyderabad, Telangana, India, Email:

Editorial

Data mining is a subset of business analytics. Data mining is the activity that a business engages in to find meaningful information from all sources of data, which can be loosely referred to as raw data, by employing intelligent and scientific techniques, also known as algorithms. When imagining data mining as a machine, the raw data becomes the input, the data mining activity becomes the task the machine is designed to perform, and the output from the machine is actionable data, or data that can be used to make strategic or tactical decisions that have a positive impact on the bottom line.

When imagining data mining as a machine, the raw data becomes the input, the data mining activity becomes the task the machine is designed to perform, and the output from the machine is actionable data, or data that can be used to make strategic or tactical decisions that have a positive impact on the bottom line. So, what does the machine in the figure below represent? In this oversimplified model, the machine is the tool used to execute the various methods and techniques used in data mining. This discussion will center on the machine we identified as the tool used to carry out the data mining techniques. Data mining tools are software programmers that aid in the design and execution of data mining techniques used to create and test data models. It is typically a framework, such as R studio or Tableau, with a suite of programs to aid in the development and testing of a data model.

There are numerous tools on the market, both open source and proprietary, with varying degrees of sophistication. At its core, each tool aids in the implementation of a data mining strategy, but the difference lies in the level of sophistication you, the software's customer, require. There are tools that excel in a specific domain, such as the financial or scientific domains. A data science software platform that provides an integrated environment for data modeling stages such as data preparation, data cleansing, exploratory data analysis, visualization, and more. Machine learning, deep learning, text mining, and predictive analytics are some of the techniques that the software can assist with.

GUI tools that guide you through the modeling process. This open-source framework, written entirely in Java, is extremely popular in the data mining world. Oracle, the world leader in database software, combines its database technology prowess with analytical tools to bring you Oracle Advanced Analytics Database, which is included in the Oracle Enterprise Edition. It includes a number of data mining algorithms for classification, regression, prediction, anomaly detection, and other tasks. This is proprietary software that is supported by Oracle technical staff in order to assist your company in building a robust data mining infrastructure at the enterprise scale. When it comes to large enterprises, IBM is once again a household name in the data space. It works well with leading technologies to create a strong enterprisewide solution. IBM SPSS Modeler is a visual data science and machine learning solution that helps data scientists reduce time to value by speeding up operational tasks. From drag-and-drop data exploration to machine learning, IBM SPSS Modeler has you covered [1-5].

References

1. Corbett, Albert T, and John R. Anderson. "Knowledge tracing: Modeling the acquisition of procedural knowledge." User Model User Adapt Interact 4 (1994): 253-278.

Google Scholar, Crossref

2. Feng, Mingyu, Neil Heffernan and Kenneth Koedinger. "Addressing the assessment challenge with an online system that tutors as it assesses." User Model User Adapt Interact 19 (2009): 243-266.

Google Scholar, Crossref

3. Koedinger, Kenneth R, Elizabeth A McLaughlin and Neil T Heffernan. "A quasi-experimental evaluation of an on-line formative assessment and tutoring system." J Educ Comput Res 43 (2010): 489-510.

Google Scholar, Crossref

4. Martin, Brent, Antonija Mitrovic and Kenneth R Koedinger,et al. "Evaluating and improving adaptive educational systems with learning curves." User Model User Adapt Interact 21 (2011): 249-283.

Google Scholar, Crossref

5. Romero, Cristóbal, and Sebastián Ventura. "Educational data mining: a review of the state of the art." IEEE Trans. Syst. Man Cybern 40 (2010): 601-618.

Google Scholar, Crossref