Chapter 4

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Maseno University *

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Nov 24, 2024

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Running head: DATA MINING 1 Data Mining Student’s Name Institution

DATA MINING 2 Discussion Question 1 1: Data Mining Data mining is the technique of analyzing data to identify anomalies, trends, and patterns. It is also defined as the concept of examining large previous datasets to extract knowledge ( Sharda, Delen & Turban, 2020) . The process of data mining has many definitions since individuals apply different techniques and tools to mine data. The application of data mining in various sectors also influences its definitions. 2: Popularity of Data Mining Data mining is rapidly increasing in popularity due to many reasons. First, the increasing competition in the global arena resulting from consumers increasing preferences has taken over the marketplace. This leads to business entities embracing data mining as the solution to ensure their competitive advantage. Secondly, the essence of data mining in providing decision-makers with informed insights allows them to make sound business decisions. Many organizations have realized the importance of data mining in decision-making, leading to its popularity ( Sharda, Delen & Turban, 2020) . Data mining is also increasing in popularity due to its important function of consolidating and integrating databases to allow a single view of business elements like customers, transactions, and vendors, among others. By embracing data mining, organizations can now track various items in their data warehouse with a single view. Another reason why data mining is increasing in popularity is its significance in reducing hardware and software costs that would be rather used for storing and processing data ( Sharda, Delen & Turban, 2020) . Data mining promotes data storage in a single warehouse, where it can be easily retrieved if need be. Finally, as move businesses move towards the Internet of Things (IoT), business practices move

DATA MINING 3 from physical to digital, and data mining remains the only solution to ensuring efficiency in business processes. 3: Data Mining Software Choosing between data mining software is a crucial task that an organization should pay key attention. Before deciding to buy a data mining software, an organization should first consider the standards of using the software. An organization may also subject its expertise to the use of the software and run some analyses to determine if it corresponds to the software's business needs. The organization can then conduct a cost/benefit analysis to determine the software's effectiveness in the company and decide whether to buy or not. 4: Data Mining versus other Analytics Data mining differs from other forms of analytical technics and tools in a variety of ways. Data mining is different from other forms of analytical tools and techniques such that data mining entails a combination of several analytical concepts and tools ( Sharda, Delen & Turban, 2020) . Data mining also appears to be different from other forms of analytics. It identifies patterns in large data sets, which is a case in other analytics that give insights. 5: Main Data Mining Methods Data mining methods are classified under three main categories, which include prediction, clustering, and association. Prediction is a data mining method that informs about the future. This method utilizes facts rather than guessing to forecast about the future. On the other hand, clustering is a data mining method that allows discovering groups of entities that share characteristics. The third method of data mining is association, which establishes relationships between items.

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DATA MINING 4 The difference between these three data mining methods is that whereas the prediction method forecast the future based on previous datasets, clustering divides patterns record into clusters with each segment sharing some characteristics ( Sharda, Delen & Turban, 2020) . Conversely, the association method of data mining discovers items that are related in a large data set. Exercise 1: Developments in Data Mining and Predictive Models The increasing application and popularity for data mining of big data have powered advancements in predictive models. Developments in data mining and predictive modelling can be viewed in terms of their applications and their accuracy in different fields. Some of the advances in predictive models include using advanced use of Bayesian networks for predictive purposes. Numerous extensions have been made in the Bayesian classier to enhance the functionality of the model ( Arrazola et al., 2013) . Other advances are viewed in terms of hyperplanes' usefulness in generating trees essential in carrying out regression tasks. On the other hand, advancements in data mining are evident in various methods used in data mining. The emergence of multimedia data mining is a significant development. Multimedia data mining entails data extraction from several multimedia sources like audio, video, and image ( Almasoud, Al-Khalifa & Al-Salman, 2015) . Another advancement in data mining is the discovery of the ubiquitous data mining method. Ubiquitous data mining is a mining method that entails the use of mobile devices to obtain information from datasets.

DATA MINING 5 References Almasoud, A. M., Al-Khalifa, H. S., & Al-Salman, A. (2015, October). Recent developments in data mining applications and techniques. In 2015 Tenth International Conference on Digital Information Management (ICDIM) (pp. 36-42). IEEE. Arrazola, P. J., Özel, T., Umbrello, D., Davies, M., & Jawahir, I. S. (2013). Recent advances in modelling of metal machining processes. CIRP Annals , 62 (2), 695-718. Sharda, R., Delen, D., & Turban, E. (2020). Analytics, data science, & artificial intelligence .

Chapter 4

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