Microsoft SQL Server Data Mining plus new examples for most algorithm then share the diagram by saving it as a web page or PDF document. Data Mining with Microsoft SQL Server shows you how to: • Apply data mining solutions using Microsoft Excel • Use the data mining Add-ins for Microsoft . Creating an Analysis Services Project (Basic Data Mining Tutorial). .. Microsoft SQL Server Data Mining resources. Creating and Querying Data 9/25/ 10/25/ 11/25/ M North America. $TIME. Amount. 7/25/.
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Preparing and Mining Data with. Microsoft® SQL Server™ and Analysis Services. Seth Paul. Nitin Guatam. Raymond Balint. The Data Mining Client addin enables you to go through the full data mining lifecycle within Excel by using your spreadsheet data. If you have the Data Mining . Estimation, classification and prediction are data mining tasks that have a target bestthing.info SQL Server Data Tools uses Microsoft Visual Studio (VS) as the Integrated.
Here, it is Reseller Sales Amount. Usually, a dimension will have two, and often more, attributes strictly speaking, we should call them attribute hierarchies. In addition, many dimensions in a cube design have user hierarchies that are composed of two or more attribute hierarchies. We will look at user hierarchies shortly. Syntax -- multiple-hierarchy dimension select [Product] -- dimension on columns from [Adventure Works] Result Analysis This error message is deliberate and informative.
To avoid any ambiguity, it is essential that you include the hierarchy name with the dimension name if the dimension has more than one hierarchy. The Product dimension has many attribute and user hierarchies.
The next query addresses this problem. This will eliminate the error message seen in the previous query. In this case, the hierarchy is an attribute hierarchy called Category. The result, once again, depicts the single member at the All level. It seems like a good idea to always include both the dimension and the hierarchy name in your queries.
This was omitted in the previous query. Four queries to try! Hopefully, this one will show you just how it is done—and how it is not done. Some of these queries will work, and one or two may fail. Syntax select [Product]. Possibly, the best one to use is [ all ]. One that may cause you the odd headache is [all products]. Secondly, if your SSAS is case-sensitive, it will fail if you get the capitalization wrong. Fortunately, most SSAS installations are case-insensitive. Case-sensitivity, or the lack of it, is a choice made during installation.
Hierarchies contain one or more levels in reality I should say hierarchies contain two or more levels, because there is always, by default, an All level for every hierarchy.
Levels contain members. If you are new to multidimensional cubes, that may well sound suitably obscure. It is not absolutely necessary to understand concepts and theory and jargon in order to become productive in MDX. So, maybe, just try the query. Once you do master the concepts, your MDX will become even easier to write and you will be even more productive!
The conceptual nature of multidimensional data is beyond the scope of this book. Okay, to really confuse you, because levels have members and hierarchies have levels, then it follows that hierarchies have members too!
MacLennan J., Tang Z., Crivat B. Data Mining with Microsoft SQL Server 2008
Informally, this is a function. More formally, it is a property function because it is preceded by the dot notation. The sample query is asking for the members of the Category hierarchy an attribute hierarchy composed of two levels. Syntax -- specifying. It is also an attribute hierarchy. So, its members include the All level member as well as the individual category names as members at the Category level. Notice, the All Products member appears first.
If you had omitted the. More on. Although it requires more work, it is beginning to look like business intelligence. It makes sense to a business user, hopefully. You need to see the categories. One of the easiest ways of doing this is to use the. The following query is asking for the members of the Category level of the Category hierarchy of the Product dimension.
Note the absence of All Products this time.
If you are completely new to MDX, then references such as [Product]. But, if you understand the rules, it becomes somewhat clearer.
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Try running them individually. The first query will produce an error. The second query will return some cells. Syntax -- 2 levels together select [Product]. To fully understand it, you need to come to grips with some multidimensional concepts.
I guess it depends to some extent on which part of the English-speaking world you live in. The second query is quite a useful one. It shows each category and then the total for the four categories.
You saw similar results in an earlier query, using [Product]. For example, we can position the All Products member after the individual categories.
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Why the delimiters? If you are not interested in the theory, then by all means move on. If you do, it is strongly recommended that you return later to this paragraph. It points to one or more cells containing one or more measures.
In other words, it acts as a coordinate. When a member acts as a coordinate, it is referred to as a tuple pronounce that how you will! It is one-dimensional. In a multidimensional cube, you also need the coordinates or tuples of all the dimensions to exactly specify a particular cell containing data. In addition, if each cell contains more than one measure, it is also necessary to specify which measure so a cell in the query result shows one and only one number.
Be sure to leave the Year column set as the Key.
Click Next. Leave the Year column set to Long, because the time series works best with a floating point data type. It might return errors with long integer values. In the Completing the Wizard page, you need to give the mining structure and mining model appropriate names.
The mining structure will become the container for multiple models, and each model uses a specific model algorithm that should be incorporated into the name. The name of the structure should also reflect the name of the table or view on which it's based.
Click Finish to create the data mining structure. With the mining structure created, it's time to process and explore it. You'll be prompted to update and process the model. Accept all the prompts. When the Process Mining Structure dialog box opens, click the Run button. In the Process Progress dialog box, you can expand the nodes in the tree view to see the details while the structure and model are being processed, as shown in Figure 9.
Click Yes to build and deploy the model and to update any objects. When the Mining Model Viewer is displayed, you'll see a line chart like that in Figure 10, which shows historical and predicted tornado data by year for the states in Tornado Alley. Specifically, it shows the number of tornados as a percentage of deviation from a baseline value in each state from through , with predictions for five more years.
The first thing you're likely to notice is a rather tall spike prediction for Kansas. We know that this prediction is wrong because it was forecasting the future from and we know that there wasn't roughly a 5, percent increase in tornados i. This brings us back to Dr.
Box's statement that "all models are wrong but some are useful. I'll deal with this a little bit later. For now, clear the check box next to KS.
As you can see in Figure 11, the projected trend is much better now.
Next, clear all the check boxes, except for SD, which will isolate the results for South Dakota. Use the Prediction steps option to increase the prediction steps to Notice that you're now projecting future tornado patterns 25 years into the future, to the year It's important to note that unless there's a very strong and regular pattern in the historical data, the time series algorithm might not be accurate beyond a few periods.
However, looking at several periods will help you spot a predicted pattern and verify that the time series algorithm is doing its job. Check the Show Deviations box to display the range of confidence in the accuracy of the predicted values.
Figure 12 shows the results. South Dakota has had a fairly regular pattern of tornado activity from to , which gives the time series algorithm a lot to work with.
Even if you were to move the line to the upper or lower end of the deviation range, you could still see the predicted pattern. Now, back to Kansas. Remember the big spike predicted for ? Clearly, the time series algorithm is having problems making a prediction with this data when using the default settings.
This scenario is actually very common, and you just need to offer some guidance to get it on the right track. Every one of the nine Microsoft data mining algorithms has a different set of parameters that do different things. These are the knobs and switches that control the behavior of the complex mathematical processes and rules used to make predictions.
There are a lot of complex details that warrant further discussion and a deeper understanding. Making adjustments to these settings can either make a model work well or make the model go crazy.
I encourage you to experiment with different settings by making a change and reprocessing the model. It can be time consuming, but this is an important part of the process for creating a useful data mining solution. By leaving these unconstrained, the model algorithm is blowing a fuse and giving crazy results.
Reprocess and browse the model.
This time the prediction results for KS are in a moderate range. If you increase the number of prediction steps, you'll see that the model seems to be making a reasonable set of predictions for annual tornado counts for the next 25 years. However, if you select the Show Deviations check box, you'll see that the algorithm has very little confidence in its ability to make a prediction with the information provided, as Figure 13 shows.
Why can't this model predict the future of tornado activity in Kansas? I posed this question to Mark Tabladillo, who does a lot of work with predictive modeling and statistical analysis.
He said, "Typically, we do not get 'whys' in data mining. The desire to explain "why" is human nature, but a scientific explanation might not always be possible.
According to Tabladillo, " Correlation and causality are different, and most data mining results are correlation alone. Through time and patience, we can make a case for causality, though people, from academics to news reporters, are tempted to jump to a causal conclusion, either to project that they have done that requisite homework or simply to be the first mover-of-record.
In this case, it might be that Kansas doesn't have a strong fluctuating pattern of annual tornado counts like South Dakota does. Keep in mind that, so far, you're considering only the absolute count of all tornados in each state, aggregated over a year. You're not considering other attributes such as each tornado's category, strength, or duration or the damage caused by each tornado. This information is in the data and can be used to create more targeted models.
I'm looking out my office window at Mount St. Helens, here in Washington State. Thirty-three years ago I watched it erupt and remember the events leading up to that event. I've had a fascination with volcanos and earthquakes ever since. During the evening news, before and shortly after the eruption, the United States Geological Survey USGS would report the location and characteristics of the earthquakes that it studied in its effort to learn more about what was going on with the mountain and perhaps other volcanos in the region.
I'll show you how to use the Excel data mining add-ins to analyze the potential association between volcanos and earthquakes by looking at how many days each earthquake occurred before each volcano eruption, as well as its depth, magnitude, and distance from the volcano. Before the Excel data mining add-ins can be used to generate mining models, a feature must be enabled on the SSAS server. As Figure 14 shows, change this property to true, then click OK to save the setting.
As Figure 15 shows, this tab includes many ribbon buttons organized into groups. Note that when I created Figure 15, I had already set up a default connection. When a default connection isn't configured, the Connection group shows a button like the one in Figure You can specify a default connection by clicking the button labeled and entering the requested information in the dialog box that appears. The next step is to add the data source, which is the Weather and Events database in this case.
In Excel, place your cursor in the top-left cell of a blank sheet.
Getting Started with Data Mining in SQL Server
On the first page of the wizard, provide the name of the SQL Server instance or server. If you're working on a local development machine, enter LocalHost. On the last page, click Finish to save the connection and close the wizard. Click OK to import this data into the worksheet. At this point, you can create a cluster model. Place the cursor anywhere in the table you imported.
Click Next twice to accept the current range as the table for the model. Click Next again and set the Percentage of data for testing value to 0. In a production solution, it would be best to use the default setting or to manually create separate training and testing sets. However, for this example, you need to analyze all the available data, which is why you just set the value to 0.
Click the Finish button to complete the wizard. After the Cluster Wizard creates the cluster model, it opens a Browse window that contains the results. As you can see in the Cluster Diagram tab in Figure 17, the cluster algorithm found six different clusters with similar attribute profiles.
The more densely populated clusters have darker backgrounds. The Cluster Profiles tab shows the characteristics of each cluster. As you can see in Figure 18, Cluster 1 includes several volcanos and 79 related earthquakes.
As the turquoise diamond in the DaysBeforeEruption row shows, those earthquakes occurred several days before the eruption. Each turquoise-colored diamond displays the range of values for a particular variable, with the mean value at the midpoint of the diamond. A short diamond represents a very narrow range of values, and a tall diamond indicates that the values are indiscrete. The depth and magnitude of the earthquakes in Cluster 1 were consistently shallow and low, but the distance from the mountain was large—in the to kilometer range.
Other clusters of volcanos and earthquakes had very different characteristics, which a geologist, seismologist, or volcanologist might find useful for categorizing future volcanic eruptions and predicting their relative behavior.
Note that you can give the clusters more descriptive names. To do so, simply right-click the heading and choose Rename Cluster. Clusters can be compared to one another on the Cluster Discrimination tab, which Figure 19 shows. The blue bars show the degree to which the variables differ in favor of one cluster or another. To begin, put your cursor in the table you imported from the Weather and Events database. Click the Analyze Key Influencers button. This opens another dialog box named Advanced Column Selections, which contains a list of column names.By default, it returns the default member of the attribute hierarchy.
Specifically, it shows the number of tornados as a percentage of deviation from a baseline value in each state from through , with predictions for five more years. Clearly, the time series algorithm is having problems making a prediction with this data when using the default settings.
It is fairly clear that the cell returned is for Bikes! George Box, a statistician best known for pioneering time-series predictions, once said, "Essentially, all models are wrong but some are useful. As Figure 15 shows, this tab includes many ribbon buttons organized into groups.
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