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So below are the questions and its proposed answer which will help reader to improve fundamentals of Data Mining in SSAS. Data Mining is nothing but some kind of logic or algorithm running on genuine historical data to bring out some kind of prediction. So Data Mining means mining or hunting from the genuine historical data using some algorithms to do prediction, forecasting, etc. Now to make that algorithm alive or useful that algorithm has to be trained. Training an algorithm means that algorithm is running on a genuine historical data and as soon as it runs on the genuine data the algorithm starts working and puts some mind into it.
By running on the data it will try to figure out what will be the future prediction of the data. In short we say that to train an algorithm we have to make that algorithm to run on genuine data and this is how the algorithm gets trained. Now trained means we are artificially putting some intelligence on a dead algorithm so that it can think in terms of that data to do forecasting, predictions, etc. In what scenarios would we use Time Series Algorithm?
The Time Series Algorithm is used in scenarios where we want to do forecasting like about the annual sales, annual sales profit and various other forecasting. What type of project will we select for Data Mining? For Data Mining we will select our regular Multidimensional project as we used to select the project when we were doing SSAS. For doing Data Mining Cube is compulsory?
Now usually it is not compulsory to have a Cube or create a Cube for doing Data Mining. But professionally in the industry for doing Data Mining Cube is necessary. Because data coming from the Cube will be faster, so as a best industry practice Cube is necessary for Data Mining but it is not compulsory because Data Mining can also be done without Cube. Sequence Clustering Algorithm is a combination of sequence analysis and clustering. It identifies clusters of similarly ordered events in a sequence and these clusters can be used to predict events based on their characteristics.
So this algorithm will see which event of purchasing from the shop is most used and do analysis. So Continuous means if we have Sales Amount so continuous data type will be like 100, 100. And Key Time Type means suppose if we have Sales Year then the Key time data type will be like Saturday, Sunday ,Monday, Tuesday, etc. It is kind of a discreet data type. What is Model in Data Mining? Model is nothing but it is a thought process which will make us understand that how that thing will look like in real world or when it executes.
For example if we want to build a car first we will build a model of a car. Now in SSAS we have an algorithm and this algorithm is dead by itself so when this logic runs through the historical data it becomes alive or we can say get trained and have the thought process. And this thought process it uses to predict. So basically it tells us how our algorithm has been trained and does the prediction in an intelligent way.
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