1 / 20

An Introduction to Data Mining Hosein Rostani Alireza Zohdi

An Introduction to Data Mining Hosein Rostani Alireza Zohdi Report 1 for “advance data base” course Supervisor: Dr. Masoud Rahgozar December 2007. Outline. Why data mining? Data mining applications Data mining functionalities Concept description Association analysis

pegeen
Download Presentation

An Introduction to Data Mining Hosein Rostani Alireza Zohdi

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. An Introduction to Data Mining Hosein Rostani Alireza Zohdi Report 1 for “advance data base” course Supervisor: Dr. Masoud Rahgozar December 2007

  2. Outline • Why data mining? • Data mining applications • Data mining functionalities • Concept description • Association analysis • Outlier Analysis • Evolution Analysis • Classification • Clustering

  3. Why data mining? • Motivation: • Wide availability of huge amounts of data • Need for turning data into useful info & knowledge • Data mining: • Extracting or “mining” knowledge from large amounts of data • Knowledge : useful patterns • Semiautomatic process • Focus on automatic aspects

  4. Data mining applications • Prediction. Examples: • Credit risk • Customer switching to competitors • Fraudulent phone calling card usage • Associations. Examples: • Related books for buy • Related accessories for suggest: e.g. camera • Causation discovery: e.g. medicine • Clusters. Example: • Clusters of disease

  5. Data mining functionalities • Concept description • Characterization & discrimination • Association analysis • Outlier Analysis • Evolution Analysis • Classification and Prediction • Clustering

  6. Concept description • Description of concepts • summarized, concise & precise • Ways: • Data characterization • Summarizing the data of the target class in general terms • Data discrimination • Comparison of the target class with the contrasting class(es) • Examples of Output forms: • Pie charts, bar charts, curves & multidimensional tables

  7. Association analysis • Mining frequent patterns • For discovery of interesting associations within data • Kinds of frequent patterns: • Frequent itemset • Set of items frequently appear together. E.g. milk and bread • Frequent subsequence • E.g. pattern of customers’ purchase: • First a PC, then a digital camera & then a memory card • Frequent substructure • Structural forms such as graphs, trees, or lattices • Support and confidence

  8. Outlier Analysis • Outliers: • data objects disobeying the general behavior of data • Approaches to outliers • Discard as noise or exceptions • Keep for applications such as fraud detection • Example: detecting fraudulent usage of credit cards • Ways: • Using statistical tests • Using distance measures • Using deviation-based methods

  9. Evolution Analysis • Description and modeling of trends • For objects with changing behavior over time • Ways: • Applying other data mining tasks on time related data • Association analysis, classification, prediction, clustering & … • Distinct ways • time-series data analysis • sequence or periodicity pattern matching • similarity-based data analysis • Example: stock market: predict future trends in prices

  10. Classification and Prediction • Classification: • Process of finding a model that distinguishes data classes • Purpose: using the model to predict the class of new objects • Deriving model: • Based on the analysis of a set of training data • data objects with known class labels • Example: • In a credit card company • Classification of customers based on their payment history • Prediction of a new customer’s credit worthiness

  11. Classification • A two-step process for classification: • First: Learning or training step • Building the classifier by analyzing or learning from training data • Second: classifying step • Using classifier for classification • Accuracy of a classifier (on a given test set) • Percentage of test set tuples correctly classified by classifier • Classification methods: • Decision tree, Naïve Bayesian classification, Neural network, k-nearest neighbor classification, …

  12. Decision tree • Decision tree induction : • Learning of decision trees from class-labeled training tuples • Decision tree: A flowchart-like tree structure • Internal nodes: tests on attributes • Branches: outcomes of the test • Leaves: class labels • Usage in classification: • Prediction by tracing a path from the root to a leaf node • Testing attribute values of new tuple against decision tree • Easily converting Decision tree to classification rules

  13. Decision tree example: Does a customer buys a computer?

  14. Bayesian Classification • Bayesian classification • Predicting the probability that a new tuple belongs to a particular class • High accuracy and speed in large databases • Based on Bayes’ theorem • Conditional probability • Naïve Bayesian classifier • Assumption: class conditional independence • Good for Simplifying computations

  15. Clustering • The process of grouping a set of physical or abstract objects into classes of similar objects • Generating class labels for objects currently without label • Clustering based on this principle: • Maximizing the intraclass similarity and • Minimizing the interclass similarity • Clustering also for facilitating taxonomy formation • Hierarchical organization of observations

  16. Restaurant database Preprocessing Object View for Clustering Clustering A Set of Similar Object Clusters Summarization White Collar for Dinner Retired for Lunch Young at midnight An example: clustering customers in a restaurant

  17. Steps of database Clustering • Define object-view • Select relevant attributes • Generate suitable input format for the clustering tool • Define similarity measure • Select parameter settings for the chosen clustering algorithm • Run clustering algorithm • Characterize the computed clusters

  18. Challenge: database clustering • Data collections are in many different formats • Flat files • Relational databases • Object-oriented database • Flat file format: • The simplest and most frequently used format in the traditional data analysis area • Databases are more complex than flat files

  19. Challenge: database clustering (cont.) • Challenge: Changing clustering algorithms to become more directly applicable to real-world databases • Issues related to databases: • Different types of objects in DB • Relationships between objects: 1:1, 1:n & n:m • Complexity in definition of object similarity • Due to the presence of bags of values for an object • Difficulty in selection of an appropriate similarity measure • Due to the presence of different types for attributes of objects

  20. Refferences • Han, J., Kamber, M., Data Mining: Concepts and Techniques, Second Edition, Elsevier Inc., 2006, 770 p., ISBN 1-55860-901-3. • Silberschatz, A., Korth, F., Sudarshan, S., Database System Concepts, Fifth Edition, McGraw-Hill, 2005, ISBN 0-07-295886-3. • Ryu, T., Eick, C., A Database Clustering Methodology and Tool, in Information Sciences 171(1-3): 29-59 (2005).

More Related