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Discussion on: Identify and describe some approaches to Data Mining and Analytics

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Angel Paudel

Data mining helps in finding new patterns and relationship from the data. For predictive modeling applications and complex data mining, analytics tools such as spreadsheets with statistics are used. It opens a new and exciting way for a company to do business by allowing them to dig into the information and find new, revolutionizing conclusions (O’Brien & Marakas, 2013). Data analytics, on the other hand, helps in making conclusions by examining and analyzing raw data. It scans and filters data to come to a conclusion on things (Duan & Xiong, 2015). It helps the business make an informed decision like providing a platform for science centered industries to validate current theories.

Tools such as regression, classification, decision tree, neural network, and market basket analysis are used as approaches for data mining (Mikut & Reischl, 2011). Regression is used to predict continuous value often using scatter plot while classification makes use of discrete categories to assign data. An example of these would be in case of buying a house, if you’re looking at the price, location, size; it’ll be called as a regression but if you’re looking at the crime rate in the area, walkability and alike; that’s called as classification. Decision tree on the hand looks at the possible outcomes of each of the statement or even the outcome itself to have multiple branches with each depicting a different situation and each having a branch to more. Neutral network, inspired by biological neural network helps in determining the class using a linear combination of attributes. And, the market basket analysis is one of the most commonly used approaches in data mining. It helps in determining what products a customer purchases along with another product. This can then be used as part of cross-selling, product placement and even affinity promotion to increase the sales of the business.

There are several approaches that a company can make use of to gather all the data, group them and to visualize it with different approaches to data analytics. One of the approaches would be with the use of import.io. It helps in grabbing information from different websites. If you’re looking for information about mobile phones, the application would take that keyword and look out while pulling data relevant for you. Another tool would be NodeXL. It visualizes networks and relationships while also providing exact calculation. With the tool, if you’re looking for people talking about your product in twitter, you can feed the system with the keyword and it’ll provide you with a visual representation via a graph of people talking about it on twitter. Furthermore, google search operators can be another tool or approach. This tool allows filtering results that are relevant and important to the organization. An example would be that you can filter the search to just include the results of the last one year with the word monthly report included in each of the search terms. Google fusion table is another approach. It allows in visualizing the data as well. With the use of the tool, people or a company can gather all data, visualize and even share it through the platform. Finally, there is another tool in OpenRefine, it can also be considered as housecleaner software. It helps in checking spelling, spaces and other errors by grouping similar entities and making them all ready for analysis. Consider that there are two differently formatted reports made by two different people of the same thing, one of them have capitalized each word while the other has extra spaces between each data. With the use of this approach, a company can ensure that the formatting is the same and constant by nicely grouping the data.

References

Duan, L., & Xiong, Y. (2015). Big data analytics and business analytics. Journal Of Management Analytics , 2 (1), 1-12.

Mikut, R., & Reischl, M. (2011). Data mining tools. Wiley Interdisciplinary Reviews: Data Mining And Knowledge Discovery , 1 (5), 431-443.

O’Brien, J., & Marakas, G. (2013). Introduction to information systems (6th ed.). New York: McGraw-Hill.