Association Rule Learning. The association between variables is a popular method for helping businesses answer questions about bundling products. The study produced by the IBM Research Center in 1993 highlights the application of this analysis using a shopping basket analogy. A classic example provided of this data-mining technique is the statement that “90% of transactions that purchase bread and butter also purchase milk” (Agrawal, Imielinski, & Swami, 1993). Modern examples of application using the Association Rule Learning technique are available on Amazon.com. When a user browses a product on Amazon.com, they are presented with additional products they may be interested in purchasing. The products presented to the user in this section are a direct application of the Association Rule Learning data-mining algorithm.
Time-series Analysis. So far, all of the data-mining tasks presented are related to analyzing the relationship between variables. Time-series analysis differs in that it “accounts for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for.” (NIST/SEMATECH, 2003)
“There are two main goals of time series analysis: (a) identifying the nature of the phenomenon represented by the sequence of observations, and (b) forecasting (predicting future values of the time series variable).” (Statistica) Examples of these goals are often used in forecasting measures for business. All events, which can be measured over time, benefit from time-series analysis and allow for great business decision-making improvements. A specific example could be looking at call volume for a customer-service call center. The amount of calls handled by customer service agents in the call center is going to change day by day. Variability in terms of weekends, holidays, and seasonality based on the nature of the business are all factors that the time-series analysis example considers. The time-series analysis could then be used by call center managers to staff the appropriate number of customer service agents to handle the amount of calls coming in.
Data Mining Algorithms
Decision Trees. Decision tree algorithms are a category of algorithms used best by classification, regression, and association data-mining tasks. “Classification and regression data-mining tasks can use multiple data-mining algorithms including Bayesian with K2 prior, Uniform prior, Entropy-based, Bayesian Gaussian for regression trees, and Complete/simple-binary splits.” (Pyungchul Kim, 2004, p. 25) In each case the algorithm attempts to create a tree structure that can be presented graphically to help business decision makers understand the correlation between variables in the data.
Figure – Decision Tree Visualization
Naïve Bayes. Naïve Bayes is an independent features classification algorithm used in data mining based on the Bayes theorem. The Bayes Theorem is a probability-based theorem used when determining “k mutually exclusive states of nature” (Bowerman, O'Connell, & Orris, 2009, p. 199) for a given outcome. “The Naive Bayes is often used as a baseline in text classification because it is fast and easy to implement. Its severe assumptions make such efficiency possible but also adversely affect the quality of its results.” (Rennie, Shih, Teevan, & Karger, 2003) In business this algorithm is can be used for advanced data exploration and quick results from large sets of data.
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