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Knowledge Discovery Using Data Mining

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-KDD process …

-KDD process
-ETL tools
-Data Mining methodologies

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  • 1.
    • CmpE 274 –Business Intelligence Technologies.
  • 2.
    • Jinal Shah (ID-005242095)
    • Sohel Dadia (ID-005177251)
    • Ankit Khera (ID-005226495)
    • Riddhi shah(ID-005359513)
    • Vivek Modi(Id-005208581)
    • Parth Vora (ID-005169100)
  • 3.
    • --Knowledge Discovery??
    • --KDD Process
    • --Data Mining Algorithms
    • --Different forms of Mining Models
    • --Classification of Algorithms
    • --Weka
    • --DEMO
    • -- Questions??????
  • 4.
      • It is a process of searching knowledge from data and it focuses on the high level application of various data mining methods.
      • It main goal is mining information from raw data in the context of large databases.
      • It makes use of different data mining algorithms to extract information.
  • 5.
      • KDD is used in machine learning, pattern-recognition, databases , AI, MIS and lot of other applications.
      • It does the transformation according to the measures and thresholds.
      • It also takes in to account the preprocessing, sub-sampling, and transformation of the database if required.
  • 6.
    • 1. Data Cleaning
    • 2. Data Integration
    • 3. Data Selection
    • 4. Data transformation
    • 5. Data Mining
    • 6. Pattern Evaluation
    • 7. Knowledge Presentation
  • 7.  
  • 8.
    • The data mining algorithm is the mechanism that creates mining models.
    • To create a model, an algorithm first analyzes a set of data, looking for specific patterns and trends.
    • The algorithm then uses the results of this analysis to define the parameters of the mining model.
  • 9.
    • Decision Trees and Rules
    • Non-linear regression and classification Methods
    • Example-based Methods
    • Probabilistic Graphical Dependency Models
    • Relational Learning Models
  • 10.
    • A set of rules that describe how products are grouped together in a transaction.
    • A decision tree that predicts whether a particular customer will buy a product.
    • A mathematical model that forecasts sales.
    • A set of clusters that describe how the cases in a dataset are related.
  • 11.
    • 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.
  • 12.
    • 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.
  • 13.
    • Apriori Algorithm :- is a classic algorithm for learning association rules.
    • Apriori is designed to operate on databases containing transactions (for example, collections of items bought by customers, or details of a website frequentation).
    • Apriori uses breadth-first search and a hash tree structure to count candidate item sets efficiently.
  • 14.
    • What is Weka ?
      • Weka is a collection of machine learning algorithms for data mining tasks.
    • Why Weka ?
      • Open Source.
      • The algorithms can either be applied directly to a dataset or called from your own Java code.
  • 15.
      • It contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.
      • It is also well-suited for developing new machine learning schemes.
  • 16.
    • Java 1.4 (or later) is required to run Weka 3.4.x and older versions.
    • The developer versions, starting with 3.5.3, also require Java 5.0.
    • Platform : Windows/ Linux
  • 17.  
  • 18.  
  • 19.  
  • 20.  
  • 21.
    • http://www.cs.waikato.ac.nz/ml/weka/
    • http://msdn2.microsoft.com/En-US/library/ms175595.aspx
    • http://en.wikipedia.org/wiki/Apriori_algorithm
    • Text book “Data Mining” by Jiawei Han and Micheline Kamber
  • 22.  

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