ALGORITHMS

• Feed Forward and Back Propagation algorithm
• Support Vector Machine
• Restricted Boltzmann Machine
• Deep Belief Neural Network
• Naive Bayes Algorithm
• Apriori Algorithm
• Decision Tree Algorithm
• Convolution Neural Network
• Recurrent Neural Network
   
  
  

  Choosing the Right Algorithm

  
  

  Choosing the best algorithm to use for a specific analytical task can be a challenge. While we can use different algorithms to perform the same business task, each algorithm produces a different result, and some algorithms can produce more than one type of result. In order to retrieve the data from the data base we can select the data mining algorithm in two ways. Those are

  
  

  1. Choosing an Algorithm by type
  2. Choosing an Algorithm by task

  
  

  Once we select the appropriate Algorithm, we use that algorithm on the data base. This algorithm analyses the data we provide, looking for specific patterns or trends. The algorithm uses the results of this analysis to define the optimal parameters for creating the mining model.

  
  

  Choosing an Algorithm by Type

  
  

  Analysis Services includes the following algorithm types:

  
  

  • Classification algorithms predict one or more discrete variables, based on the other attributes in the dataset.
  • Regression algorithms predict one or more continuous variables, such as profit or loss, based on other attributes in the dataset.
  • Segmentation algorithms divide data into groups, or clusters, of items that have similar properties.
  • Association algorithms find correlations between different attributes in a dataset. The most common application of this kind of algorithm is for creating association rules, which can be used in a market basket analysis.
  • Sequence analysis algorithms summarize frequent sequences or episodes in data, such as a Web path flow.

  
  

  Sometimes user may be apply more than one data mining algorithm to come up with the solution: for example, you could use regression to obtain financial forecasts, and use a neural network algorithm to perform an analysis of factors that influence sales. SQL Server data mining lets us to build multiple models on a single mining structure, so within a single data mining solution you might use a clustering algorithm, a decision trees model, and a naïve Bayes model to get different views on our data.

  
  

  
  

  Choosing an Algorithm by Task

  
  

  To help you select an algorithm for use with a specific task, the following table provides suggestions for the types of tasks for which each algorithm is traditionally used.

  
  

  • Predicting a discrete attribute

  Example: Decision Tree algorithm, Naïve Bayes algorithm, neural network algorithm.

  • Predicting a continuous attribute

  Example: Decision Tree algorithm, Time series algorithm, Linear Regression algorithm.

  • Predicting a sequence

  Example: Sequence Clustering Algorithm

  • Finding groups of common items in transactions

  Example:  Association Algorithm, Decision Tree algorithm

  • Finding groups of similar items

  Example: Sequence Clustering Algorithm.