Data mining is used wherever there is digital data available today. Notable examples of data mining can be found throughout business, medicine, science, and surveillance. A common way for this to occur is through data aggregation. Data aggregation involves combining data together possibly from various sources in a way that facilitates analysis but that also might make identification of private, individual-level data deducible or otherwise apparent.
Solution Walkthroughs Relational vs. Multidimensional Solutions A data mining solution can be based either on multidimensional data—that is, an existing cube—or on purely relational data, such as the tables and views in a data warehouse, or on text files, Excel workbooks, or other external data sources.
You can create data mining objects within an existing multidimensional database solution. Typically you would create a solution like this if you have already created a cube and want to perform data mining by using the cube as a data source.
When you move and backup models based on a cube, the cube must also be moved or copied. You can create a data mining solution that contains only data mining objects, including the supporting data sources and data source views, and that uses relational data source only.
This is the preferred method for creating data mining models, as processing and querying is generally fastest against relational data sources. Deploying Data Mining Solutions The instance of Analysis Services to which you deploy the solution must be running in a mode that supports multidimensional objects and data mining objects; that is, you cannot deploy data mining objects to an instance that hosts tabular models or Power Pivot data.
Therefore, when you create a data mining solution in Visual Studio, be sure to use the template, Analysis Services Multidimensional and Data Mining Project.
When you deploy the solution, the objects used for data mining are created in the specified Analysis Services instance, in a database with the same name as the solution file.
For more information about how to deploy both relational and multidimensional solutions, see Deployment of Data Mining Solutions.Comprehensive data on mines and advanced exploration projects. Includes mineral reserves, production, mining technologies, costs, mining fleet and key management.
Orange Data mining: Orange is an open source data visualization and analysis tool. Orange is developed at the Bioinformatics Laboratory at the Faculty of Computer and Information Science, University of Ljubljana.
All Data Mining Projects and data warehousing Projects can be available in this category. ashio-midori.com cse students can download latest collection of data mining project topics ashio-midori.com and source code for free.
Goal The Knowledge Discovery and Data Mining (KDD) process consists of data selection, data cleaning, data transformation and reduction, mining, interpretation and evaluation, and finally incorporation of the mined “knowledge” with the larger decision making process.
The goals of this research project include development of efficient computational approaches to data modeling (finding.
Data mining: Data mining, in computer science, the process of discovering interesting and useful patterns and relationships in large volumes of data. The field combines tools from statistics and artificial intelligence (such as neural networks and machine learning) with database management to analyze large.
Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization.