THE SQL Server Blog Spot on the Web

Welcome to - The SQL Server blog spot on the web Sign in | |
in Search

Browse by Tags

All Tags » Data Mining   (RSS)
Showing page 1 of 2 (19 total posts)
  • Data Mining Algorithms – Naive Bayes

    I am continuing with my data mining and machine learning algorithms series. Naive Bayes is a nice algorithm for classification and prediction. It calculates probabilities for each possible state of the input attribute, given each state of the predictable attribute, which can later be used to predict an outcome of the predicted attribute based on ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on September 9, 2015
  • Data Mining Algorithms – Pluralsight Course

    This is a bit different post in the series about the data mining and machine learning algorithms. This time I am honored and humbled to announce that my fourth Pluralsight course is alive. This is the Data Mining Algorithms in SSAS, Excel, and R course. besides explaining the algorithms, I also show demos in different products. This gives you even ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on July 30, 2015
  • Data Mining Algorithms – Support Vector Machines

    Support vector machines are both, unsupervised and supervised learning models for classification and regression analysis (supervised) and for anomaly detection (unsupervised). Given a set of training examples, each marked as belonging to one of categories, an SVM training algorithm builds a model that assigns new examples into one category. An SVM ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on June 23, 2015
  • Data Mining Algorithms – Principal Component Analysis

    Principal component analysis (PCA) is a technique used to emphasize the majority of the variation and bring out strong patterns in a dataset. It is often used to make data easy to explore and visualize. It is closely connected to eigenvectors and eigenvalues. A short definition of the algorithm: PCA uses an orthogonal transformation to convert ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on June 2, 2015
  • Data Mining Algorithms – EM Clustering

    With the K-Means algorithm, each object is assigned to exactly one cluster. It is assigned to this cluster with a probability equal to 1.0. It is assigned to all other clusters with a probability equal to 0.0. This is hard clustering. Instead of distance, you can use a probabilistic measure to determine cluster membership. For example, you can ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on May 12, 2015
  • Data Mining Algorithms – K-Means Clustering

    Hierarchical clustering could be very useful because it is easy to see the optimal number of clusters in a dendrogram and because the dendrogram visualizes the clusters and the process of building of that clusters. However, hierarchical methods don’t scale well. Just imagine how cluttered a dendrogram would be if 10,000 cases would be shown on ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on April 17, 2015
  • Data Mining Algorithms – Hierarchical Clustering

    Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. Dissimilarities are assessed based on the attribute values describing the objects. There are a large number of clustering algorithms. The ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on March 28, 2015
  • Data Mining Algorithms – an Introduction

    Data mining is the most advanced part of business intelligence. With statistical and other mathematical algorithms, you can automatically discover patterns and rules in your data that are hard to notice with on-line analytical processing and reporting. However, you need to thoroughly understand how the data mining algorithms work in order to ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on February 19, 2015
  • Indexing, Querying and Analyzing Text with SQL Server 2012-2014

    It is hard to imagine searching for something on the Web without modern search engines like Bing or Google. However, most contemporary applications still limit users to exact searches only. For end users, even the standard SQL LIKE operator is not powerful enough for approximate searches. In addition, many documents are stored in modern databases; ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on February 7, 2014
  • Fraud Detection with the SQL Server Suite Part 5

    This is the fifth, the final part of the fraud detection whitepaper. You can find the first part, the second part, the third part, and the fourth part in my previous blog posts about this topic. The Results In my original fraud detection whitepaper I wrote for SolidQ, I was advised by my friends to include some concrete and simple numbers to ...
    Posted to Dejan Sarka (Weblog) by Dejan Sarka on January 6, 2014
1 2 Next >
Powered by Community Server (Commercial Edition), by Telligent Systems
  Privacy Statement