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Figure 1 The CRISP-DM standard process for data mining projects
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Introduction to SSAS 2008 data mining
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Data overview and preparation
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Overviewing and preparing the data is probably the most exhaustive part of a data mining project. To get a comprehensive overview of your data, you can use many different techniques and tools. You can start with SQL queries and reports, or you can use UDM cubes. In addition, you can use descriptive statistics such as frequency distribution for discrete variables, and the mean value and the spread of the distribution for continuous variables. You can use Data Source View for a quick overview of your variables in table, pivot table, graph, or pivot graph format. Microsoft Office Excel statistical functions, pivot tables, and pivot graphs are useful tools for data overview as well. After you understand your data, you have to prepare it for data mining. You have to decide what exactly your case is. Sometimes this is a simple task; sometimes it can get quite complex. For example, a bank might decide that a case for analysis is a family, whereas the transaction system tracks data about individual persons only. After you define your case, you prepare a table or a view that encapsulates everything you know about your case. You can also prepare child tables or views and use them as nested tables in a mining model. For example, you can use an orders header production table as the case table, and an order details table as a nested table if you want to analyze which products are purchased together in a single order. Usually, you also prepare some derived variables. In medicine, for example, the obesity index is much more important for analyses than a person s bare height and weight. You have to decide what to do with missing values, if there are too many. For example, you can decide to replace them with mean values. You should also check the outliers rare and far out-of-bounds values in a column. You can group or discretize a continuous variable in a limited number of bins and thus hide outliers in the first and the last bin.
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SSAS 2008 data mining algorithms
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SSAS 2008 supports all of the most popular data mining algorithms. In addition, SSIS includes two text mining transformations. Table 1 summarizes the SSAS algorithms
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and their usage.
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Table 1 SSAS 2008 data mining algorithms and usage Usage The Association Rules algorithm is used for market basket analysis. It defines an itemset as a combination of items in a single transaction; then it scans the data and counts the number of times the itemsets appear together in transactions. Market basket analysis is useful to detect cross-selling opportunities. The Clustering algorithm groups cases from a dataset into clusters containing similar characteristics. You can use the Clustering method to group your customers for your CRM application to find distinguishable groups of customers. In addition, you can use it for finding anomalies in your data. If a case doesn t fit well in any cluster, it s an exception. For example, this might be a fraudulent transaction.
Algorithm Association Rules
Clustering
Creating mining models
Table 1 SSAS 2008 data mining algorithms and usage (continued) Usage
Algorithm Decision Trees
Decision Trees is the most popular DM algorithm, used to predict discrete and continuous variables. The algorithm uses the discrete input variables to split the tree into nodes in such a way that each node is more pure in terms of target variable each split leads to nodes where a single state of a target variable is represented better than other states. For continuous predictable variables, you get a piecewise multiple linear regression formula with a separate formula in each node of a tree. A tree that predicts continuous variables is a Regression Tree. Linear Regression predicts continuous variables, using a single multiple linear regression formula. The input variables must be continuous as well. Linear Regression is a simple case of a Regression Tree, a tree with no splits. As Linear Regression is a simple Regression Tree, a Logistic Regression is a Neural Network without any hidden layers. The Na ve Bayes algorithm calculates probabilities for each possible state of the input attribute for every single state of predictable variable. These probabilities are used to predict the target attribute based on the known input attributes of new cases. The Na ve Bayes algorithm is quite simple; it builds the models quickly. Therefore, it s suitable as a starting point in your prediction project. The Na ve Bayes algorithm doesn t support continuous attributes. The Neural Network algorithm is often associated with artificial intelligence. You can use this algorithm for predictions as well. Neural networks search for nonlinear functional dependencies by performing nonlinear transformations on the data in layers, from the input layer through hidden layers to the output layer. Because of the multiple nonlinear transformations, neural networks are harder to interpret compared to Decision Trees. Sequence Clustering searches for clusters based on a model, and not on similarity of cases as Clustering does. The models are defined on sequences of events by using Markov chains. Typical usage of Sequence Clustering would be an analysis of your company s website usage, although you can use this algorithm on any sequential data. You can use the Time Series algorithm to forecast continuous variables. Internally, the Time Series uses two different algorithms. For short-term forecasting, the Auto-Regression Trees (ART) algorithm is used. For long-term prediction, the Auto-Regressive Integrated Moving Average (ARIMA) algorithm is used. You can mix the blend of algorithms used by using the mining model parameters.
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