Data Mining: Sampling and Use of Computer Analysis

Data Mining: Sampling and Use of Computer Analysis

Data mining is the process of extracting valuable knowledge from structured, semi-structured, and unstructured data through identifying patterns, trends, and correlations in the data. Data analysis deploys physical analysis techniques like sampling or through the use of computer systems. The directions help identify the various impacts of an operation or event and assist the researcher in quality decision-making techniques. Decision making is a significant aspect of effective strategic planning. Accuracy and precision are valuable parameters for measuring the likelihood of an inference in suiting the particular strategy effectively.

The precision of analytics and measurement varies between the physical systems and computer analysis and representation of data. Accuracy of a measurement is the extent to which a measured value differs from the mean value of many quantities. Throughout the investigation process, the frequencies used to determine the mean value involve the determination of the benefits under the same conditions.

For computer systems, precision is either a single accuracy or double precision. Single precision involves the representation of values using the 32-bit representation format—this system store numerals using float nature. Single-precision stores a broad range of digits and analyses it with reliable precision. For the 32-bit system, the first bit stores the sign of the number, the next 8 bits represent the exponent of the name, and the next 23 bits represent the fraction of the Number (Zhang, Chen, & Ko, 2017). The exponent represents the range of the data, while the fraction shows the precision of the value.

Double-precision uses 64 bits to represent a number such that the range and precision of the data is improved compared to the single-precision system (Jain, Jain, & Jain, 2018). Like the single-precision system, the first bit stores the sign of the number, whether positive or negative. The next 11 bits represent the exponential while the next 52 bits represent the fraction. The precision and range of data for the double-precision system is higher than the single-precision system. The use of computer software for data analysis is much more critical than physical sampling because of the time, cost, and efficiency of handling high contents of data.

Sampling is a process of data analysis by identifying a specific population and choosing a reasonable sample from the community for study. Sampling helps reduce the data analyzed by selecting parts of the data and analyzing them separately. This representation system is essential for saving on the time and cost of data analysis, especially where the data content is high. The system would assist in the determination of valuable insights into a given system. It would however, involve bias and personal preferences of the researcher, which would shape the impact and deviate it from the actual value.

Sampling would include random sampling, whereby the researcher selects a stochastic sample from the population. He may return the sample to the community and find the probability of the retention of the precedent result. Random sampling with replacement is essential in assessing the impacts of operations that do not involve the depletion of resources. However, the majority of the real-world funds are subject to destruction and exhaustion, which makes the system sampling with replacement of an inefficient analysis process. This process is among the various systems that promote data mining.

Data mining is an essential aspect of organizational management and research because of the ability of the system to disclose valuable knowledge in unstructured data. Data mining enables researchers to identify the correlations of different entities, which allows them to determine the effects of an event or phenomenon. Identification of customer purchase patterns also helps enterprises improve their products and service delivery systems to suit their market preferences. The identification of customer transactions’ historical data helps banking and financial institutions identify possible fraud and malicious operations in blockchain systems and sales on payment cards (Amani, & Fadlalla, 2017). Data mining is an essential aspect of decision making and the prevention of possible threats to an organization.

Some of the applications of data mining are healthcare services, market basket analysis, education and institutional growth, manufacturing systems and the Internet of Things, customer relationship management, fraud detection, intrusion detection, customer segmentation, financial banking, and research analysis (Karmakar, 2018). In these applications, ordering, transformation, and analysis of data helps identify changes in trends and patterns. These changes help determine the possible misalignments, improvements, and depreciation that occur in enterprises. They reveal unnecessary operations and valuable effects of actions that guide the executives and researchers in decision making.

Taking the course and studying data mining would help me in my professional life and enterprise management because of the lessons I would gain from the studies. Data management is one of the critical factors to organizational development, and it would form a valuable foundation for my career. Recent studies show the impact of data analysis and the value of information as a company’s resource in the improvement of enterprise operations and service delivery. This system improves my skills both in my professional and personal life.

References

Jain, A., Jain, R., & Jain, J. (2018, December). Design of Reversible Single Precision and Double Precision Floating Point Multipliers. In 2018 International Conference on Advanced Computation and Telecommunication (ICACAT)(pp. 1-4). IEEE.

Zhang, H., Chen, D., & Ko, S. B. (2017). High performance and energy-efficient single-precision and double-precision merged floating-point adder on FPGA. IET Computers & Digital Techniques, 12(1), 20-29.

Karmakar, S. (2018). Application of data mining for improving participative librarianship: a brief study. Research Journal of Social Sciences, 9(10).

Amani, F. A., & Fadlalla, A. M. (2017). Data mining applications in accounting: A review of the literature and organizing framework. International Journal of Accounting Information Systems, 24, 32-58.