Cluster analysis
25 likes21,239 views
Cluster analysis is a descriptive technique that groups similar objects into clusters. It finds natural groupings within data according to characteristics in the data. Cluster analysis is used for taxonomy development, data simplification, and relationship identification. Some applications of cluster analysis include market segmentation in marketing, grouping users on social networks, and reducing markers on maps. It requires representative data and assumes groups will be sufficiently sized and not distorted by outliers.
Cluster analysis
- 1. CLUSTER ANALYSIS PREPARED BY SABA KHAN PRESENTED TO IMTIAZ ARIF ID 4640
- 2. What is Cluster Analysis? It is a descriptive analysis technique which groups objects (respondents, products, firms, variables, etc.) so that each object is similar to the other objects in the cluster and different from objects in all the other clusters. 2
- 3. What is Cluster Analysis? Cluster: a collection of data objects Similar to one another within the same cluster Dissimilar to the objects in other clusters Cluster analysis Finding similarities between data according to the characteristics found in the data and grouping similar data objects into clusters
- 4. When to use cluster analysis? The essence of all clustering approaches is the classification of data as suggested by “natural” groupings of the data themselves. Simply put when you desire the following then use Cluster analysis. Taxonomy development(segmentation) Data simplification Relationship identification Applications. It is used to segment the market in Marketing, used in social networking sites in making new groups based on users data, Flickr’s map of photos and other map sites use clustering to reduce the number of markers on a map. 4
- 5. Examples of Clustering Applications • Marketing: Help marketers discover distinct groups in their customer bases, and then use this knowledge to develop targeted marketing programs. • Land use: Identification of areas of similar land use in an earth observation database. • Insurance: Identifying groups of motor insurance policy holders with a high average claim cost. • City-planning: Identifying groups of houses according to their house type, value, and geographical location. • Earth-quake studies: Observed earth quake epicenters should be clustered along continent faults
- 6. Assumptions for Cluster Analysis. Sufficient size is needed to ensure representativeness of the population and its underlying structure, particularly small groups within the population. Outliers can severely distort the representativeness of the results if they appear as structure (clusters) that are inconsistent with the research objectives Representativeness of the sample. The sample must represent the research question. Impact of multicollinearity. Input variables should be examined for substantial multicollinearity and if present: Reduce the variables to equal numbers in each set of correlated measures. 6
- 7. HOW TO DEFINE CLUSTERS CLUSTER CLUSTER A B 1 2 3
- 8. We will now go to SPSS for analysis. Retrieve judges.sav Analyze classify Hierarchical cluster All variables. 10