Data mining
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Data mining






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Data mining Data mining Presentation Transcript

  • Data Mining
  • Agenda  What is Data Mining?  Data Mining Tasks  Challenges in Data mining
  • What is Data Mining  Data mining is integral part of knowledge discovery in databases (KDD), which is the overall process of converting raw data into useful information. This process consists of series of transformation steps from preprocessing to postprocessing of data mining results
  • Process of Knowledge Discovery in Database(KDD) Data Preprocessing Data Mining PostProcessing Normalization. Data subsetting Normalization. Data subsetting Filtering Patterns,Visualization, Pattern Interpretation Filtering Patterns,Visualization, Pattern Interpretation Inputdata Input Data Information
  • Data Mining Tasks  Data Mining is generally divided into two tasks. 1. Predictive tasks 2. Descriptive tasks
  • Predictive Tasks  Objective: Predict the value of a specific attribute (target/dependent variable)based on the value of other attributes (explanatory). Example: Judge if a patient has specific disease based on his/her medical tests results.
  • Descriptive Tasks  Objective: To derive patterns (correlation,trends,trajectories) that summarizes the underlying relationship between data. Example: Identifying web pages that are accessed together.(human interpretable pattern)
  • Data Mining Tasks [contd.]  Classification [Predictive]  Clustering [Descriptive]  Association Rule Discovery[Descriptive]  Sequential Pattern Discovery [Descriptive]  Regression [Predictive]  Deviation Detection [Predictive]
  • Classification: Definition  Classification: Given a collection of records  Each record contains a set of attributes, one of the attribute is a class.  Find a model for class attribute as a function of values of other attributes.  Goal: previously unseen records should be assigned a class as accurately as possible.  A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate
  • Classification: Example  Direct Marketing Goal: Reduce cost of mailing by targeting a set of consumers likely to buy a new product.  Approach:  Use the data for a similar product introduced before.  We know which customers decided to buy and which decided otherwise. This {buy, don’t buy} decision forms the class attribute.  Collect various demographic, lifestyle, and company-interaction related information about all such customers.  Type of business, where they stay, how much they earn, etc.  Use this information as input attributes to learn a classifier model. (from Berry & Linoff, 1997)
  • Clustering: Definition  Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that  Data points in one cluster are more similar to one another.  Data points in separate clusters are less similar to one another.
  • Clustering: Example  Document Clustering:  Goal: To find groups of documents that are similar to each other based on the important terms appearing in them.  Approach: To identify frequently occurring terms in each document. Form a similarity measure based on the frequencies of different terms. Use it to cluster.  Gain: Information Retrieval can utilize the clusters to relate a new document or search term to clustered documents.
  • Illustrating Document Clustering Category Total Articles Correctly Placed Financial 555 364 Foreign 341 260 National 273 36 Metro 943 746 Sports 738 573 Entertainment 354 278 Clustering Points: 3204 Articles Of Los Angles Times. Similarity Measure: How Many words are common in these documents. (after some word filtering) (Introduction to Data mining 2007)
  • Association Rule Discovery: Definition Given a set of records each of which contain some number of items from a given collection; Apriori principle: If an item set is frequent then its subset is also frequent TID Items 1 Bread, Coke Milk 2 3 Beer, Bread Beer,Coke, Diaper, Milk 4 Beer, Bread, Diaper, Milk 5 Coke, Diaper, Milk Rule Discovered: Milk -> Coke Diaper, Milk -> Beer
  • Other Mining Tasks in Nutshell  Sequential Pattern Discovery In point-of-sale transaction sequences,  Computer Bookstore: (Intro_To_Visual_C) (C++_Primer) --> (Perl_for_dummies,Tcl_Tk)  Regression: Neural Networks  Deviation Detection: Detect deviation from normal behavior. Eg. Credit card fraud.
  • Challenges of Data Mining  Scalability  Dimensionality  Complex and Heterogeneous Data  Data Quality  Data Ownership and Distribution  Privacy Preservation  Streaming Data
  • References  Tan, P., Steinbach, M., & Kumar, V., Introduction to Data Mining. Addison Wesley, 2006.