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Clustering and Association Modeling Using IBM SPSS Modeler (v18.1) SPVC

¿Î³Ì±àºÅ Course Code	0E048G	¿Î³Ì¼¶±ð Skill Level	Öм¶	¿Î³Ì·ÖÀà Curricula	SPSS	ÃæÊڿγ̱àºÅ Face2Face Course Code
ÊÚ¿ÎÓïÑÔ Language	Ó¢ÎÄ	ÉÏ»úʵÑé Hands-on Labs	ÓÐ	¼Û¸ñ (Ôª) Price	£¤ 1850	ʱ      ³¤ Duration	1D

¿Î³ÌÃèÊö/Course Description£º

Clustering and Association Modeling Using IBM SPSS Modeler (v18.1) SPVC

ÊڿζÔÏó/Target Audience£º

Modelers, Analysts

Ô¤±¸¼¼ÄÜ/Prerequisites£º

• Experience using IBM SPSS Modeler • A familiarity with the IBM SPSS Modeler environment: creating models, creating streams, reading in data files, and assessing data quality • A familiarity with handling missing data (including Type and Data Audit nodes), and basic data manipulation (including Derive and Select nodes)

¿Î³ÌÄ¿±ê/Skills Taught£º

Please refer to course overview

Ö÷Òª¿ÎÌâ/Course Outline£º

1: Introduction to clustering and association modeling • Identify the association and clustering modeling techniques available in IBM SPSS Modeler • Explore the association and clustering modeling techniques available in IBM SPSS Modeler • Discuss when to use a particular technique on what type of data 2: Clustering models and K-Means clustering • Identify basic clustering models in IBM SPSS Modeler • Identify the basic characteristics of cluster analysis • Recognize cluster validation techniques • Understand K-Means clustering principles • Identify the configuration of the K-means node 3: Clustering using the Kohonen network • Identify the basic characteristics of the Kohonen network • Understand how to configure a Kohonen node • Model a Kohonen network 4: Clustering using TwoStep clustering • Identify the basic characteristics of TwoStep clustering • Identify the basic characteristics of Two Step AS clustering • Model and analyze a TwoStep clustering solution 5: Use Apriori to generate association rules • Identify three methods of generating association rules • Use the Apriori node to build a set of association rules • Interpret association rules 6: Use advanced options in Apriori • Identify association modeling terms and rules • Identify evaluation measures used in association modeling • Identify the capabilities of the Association Rules node • Model associations and generate rules using Apriori 7: Sequence detection • Explore sequence detection association models • Identify sequence detection methods • Examine the Sequence node • Interpret the sequence rules and add sequence predictions to steams 8: Advanced Sequence detection • Identify advanced sequence detection options used with the Sequence node • Perform in-depth sequence analysis • Identify the expert options in the Sequence node • Search for sequences in Web log data A: Examine learning rate in Kohonen networks (Optional • Understand how a Kohonen neural network learns B: Association using the Carma model (Optional) • Review association rules • Identify the Carma model • Identify the Carma node • Model associations and generate rules using Carma