logging in or signing up SPSS sourabh_mrsc Download Post to : URL : Related Presentations : Let's Connect Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Copy embed code: Embed: Flash iPad Dynamic Copy Does not support media & animations Automatically changes to Flash or non-Flash embed WordPress Embed Customize Embed URL: Copy Thumbnail: Copy The presentation is successfully added In Your Favorites. Views: 6205 Category: Education License: All Rights Reserved Like it (2) Dislike it (0) Added: April 23, 2009 This Presentation is Public Favorites: 2 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript SPSS Tutorial : SPSS Tutorial AEB 37 / AE 802 Marketing Research Methods Week 7 Cluster analysis : Cluster analysis Lecture / Tutorial outline Cluster analysis Example of cluster analysis Work on the assignment Cluster Analysis : Cluster Analysis It is a class of techniques used to classify cases into groups that are relatively homogeneous within themselves and heterogeneous between each other, on the basis of a defined set of variables. These groups are called clusters. Cluster Analysis and marketing research : Cluster Analysis and marketing research Market segmentation. E.g. clustering of consumers according to their attribute preferences Understanding buyers behaviours. Consumers with similar behaviours/characteristics are clustered Identifying new product opportunities. Clusters of similar brands/products can help identifying competitors / market opportunities Reducing data. E.g. in preference mapping Steps to conduct a Cluster Analysis : Steps to conduct a Cluster Analysis Select a distance measure Select a clustering algorithm Determine the number of clusters Validate the analysis Defining distance: the Euclidean distance : Defining distance: the Euclidean distance Dij distance between cases i and j xki value of variable Xk for case j Problems: Different measures = different weights Correlation between variables (double counting) Solution: Principal component analysis Clustering procedures : Clustering procedures Hierarchical procedures Agglomerative (start from n clusters, to get to 1 cluster) Divisive (start from 1 cluster, to get to n cluster) Non hierarchical procedures K-means clustering Agglomerative clustering : Agglomerative clustering Agglomerative clustering : Agglomerative clustering Linkage methods Single linkage (minimum distance) Complete linkage (maximum distance) Average linkage Ward’s method Compute sum of squared distances within clusters Aggregate clusters with the minimum increase in the overall sum of squares Centroid method The distance between two clusters is defined as the difference between the centroids (cluster averages) K-means clustering : K-means clustering The number k of cluster is fixed An initial set of k “seeds” (aggregation centres) is provided First k elements Other seeds Given a certain treshold, all units are assigned to the nearest cluster seed New seeds are computed Go back to step 3 until no reclassification is necessary Units can be reassigned in successive steps (optimising partioning) Hierarchical vs Non hierarchical methods : Hierarchical vs Non hierarchical methods Hierarchical clustering No decision about the number of clusters Problems when data contain a high level of error Can be very slow Initial decision are more influential (one-step only) Non hierarchical clustering Faster, more reliable Need to specify the number of clusters (arbitrary) Need to set the initial seeds (arbitrary) Suggested approach : Suggested approach First perform a hierarchical method to define the number of clusters Then use the k-means procedure to actually form the clusters Defining the number of clusters: elbow rule (1) : Defining the number of clusters: elbow rule (1) n Elbow rule (2): the scree diagram : Elbow rule (2): the scree diagram Validating the analysis : Validating the analysis Impact of initial seeds / order of cases Impact of the selected method Consider the relevance of the chosen set of variables SPSS Example : SPSS Example Slide 19: Number of clusters: 10 – 6 = 4 Open the dataset supermarkets.sav : Open the dataset supermarkets.sav From your N: directory (if you saved it there last time Or download it from: http://www.rdg.ac.uk/~aes02mm/supermarket.sav Open it in SPSS The supermarkets.sav dataset : The supermarkets.sav dataset Run Principal Components Analysis and save scores : Run Principal Components Analysis and save scores Select the variables to perform the analysis Set the rule to extract principal components Give instruction to save the principal components as new variables Cluster analysis: basic steps : Cluster analysis: basic steps Apply Ward’s methods on the principal components score Check the agglomeration schedule Decide the number of clusters Apply the k-means method Analyse / Classify : Analyse / Classify Select the component scores : Select the component scores Select from here Untick this Select Ward’s algorithm : Select Ward’s algorithm Click here first Select method here Output: Agglomeration schedule : Output: Agglomeration schedule Number of clusters : Number of clusters Identify the step where the “distance coefficients” makes a bigger jump The scree diagram (Excel needed) : The scree diagram (Excel needed) Number of clusters : Number of clusters Number of cases 150 Step of ‘elbow’ 144 __________________________________ Number of clusters 6 Now repeat the analysis : Now repeat the analysis Choose the k-means technique Set 6 as the number of clusters Save cluster number for each case Run the analysis K-means : K-means K-means dialog box : K-means dialog box Specify number of clusters Save cluster membership : Save cluster membership Click here first Thick here Final output : Final output Cluster membership : Cluster membership Component meaning(tutorial week 5) : Component meaning(tutorial week 5) 1. “Old Rich Big Spender” 3. Vegetarian TV lover 4. Organic radio listener 2. Family shopper 5. Vegetarian TV and web hater Cluster interpretation through mean component values : Cluster interpretation through mean component values Cluster 1 is very far from profile 1 (-1.34) and more similar to profile 2 (0.38) Cluster 2 is very far from profile 5 (-0.93) and not particularly similar to any profile Cluster 3 is extremely similar to profiles 3 and 5 and very far from profile 2 Cluster 4 is similar to profiles 2 and 4 Cluster 5 is very similar to profile 3 and very far from profile 4 Cluster 6 is very similar to profile 5 and very far from profile 3 Which cluster to target? : Which cluster to target? Objective: target the organic consumer Which is the cluster that looks more “organic”? Compute the descriptive statistics on the original variables for that cluster Representation of factors 1 and 4(and cluster membership) : Representation of factors 1 and 4(and cluster membership) You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.