Innovation and Creativity for Data Mining Using Computational Statistics

Introduction

Data Mining is the concept of extracting meaningful information from a huge repository with a set of rules and algorithms. Mainly pre-processing of the data has been done on the extraction phase for the removal of duplicated and unwanted data. To perform the retrieval of data from the warehouses their knowledge has been discovered by KDD for getting better results while processing. (Regin. et al (2021)) Data mining is the main technique used for data extraction, transformation, and loading (ETL) in all disciplines to perform data storage and data retrieval effectively. Various fields are used data mining principles for accessing their data from real-world scenarios and convert them into applications. A lot of traditional approaches are used to do data processing on the larger networks but the speed of the recovery was not at the expected time interval. So they were searching for better solutions to do this with minimal time and high speed. Mining is the only solution for all and it has given the exact match of the data from the user perspective view. Moreover, a lot of information is stored in a common place for easy recovery and retrieval but the data loss and leakage were not monitored and controlled by classical methods. Data pre-processing is the main concept used in data mining techniques to avoid data duplication and repetition during data transmission. Data cleaning is also used in data mining for removing extra noisy data on the databases to overcome the data corruption problem. Data loss and leakage can be managed in data mining techniques with several algorithms and rules from classical approaches. But the main challenges and limitations of all algorithms used in data mining react with less accuracy and high latency for all real-world problem applications. So if any user wants to retrieve the data from the huge repository they must wait for a long time to get output. When considering output from that method also not inaccurate about originality. So a lot of people are working on the data mining domain to overcome all the problems and provide their solutions with recent trends or algorithms. While handling huge datasets like big data and IoT time consumed for data extraction is more rather than normal databases. For rectifying this issue in data mining recent and modern tools are used to avoid latency among the network systems. Perhaps, new innovative approaches and algorithms are implemented in day-to-day field data mining has its hype among all. The capability of categorized the data on the network systems is the basic one for all researchers. In other words, Data Mining is the backbone of all research people and industry people to do the applications of real-world problems. Anyhow data processing on the larger network is controlled and monitored by the data mining approach with its effective data processing mechanism. Data mining principles (Graif. et al (2021)) are worked on the following areas for improving the performance of the data processing in order to accessing the data.

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  Dimensionality

It is the main component of data mining techniques used to represent the information with the help of values as a variable. Their values are not constant and will be changed in every event. The analysis is made by dimensions as the values present with all data mining techniques. The values are changed every second due to the originality of the data. Because the data generated by both machines and humans are increased in size at every point of view. In this scenario, the normal standard procedures do not help with data processing whereas data mining provides much better results to this kind of problem neatly concerning time and accuracy.

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  Uncertainty

The choices of the data can be selected from KDD’s to perform data extraction as samples or values. But the output given by the system is not up to the level so their values not satisfied the requirements. Consistency-based data changes are noted at every time to avoid uncertainty over the networks. Samples have not given accurate results because of the size of the data from the repository. Sometimes wrong samples have been accessed for the mining process would lead to negative results. To overcome this problem lot of mathematical formulas and equations are used such as mean, median, average, and standard deviation (Pramanik, S. and Raja, S. S. 2020).

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  Scalability