0
Cluster analysis' is a class of statistical techniques that can be applied to data that exhibit "natural" groupings. Cluster analysis sorts through the raw data and groups them into clusters. A cluster is a group of relatively homogeneous cases or observations. Objects in a cluster are similar to each other. They are also dissimilar to objects outside the cluster, particularly objects in other clusters.
Wiki User
∙ 12y ago
Add your answer:
Earn +20 pts
Why do shops cluster together?
Shops cluster together for the convenience of shoppers.
What are the different types of statistical analysis used in the estimation of production function?
Time series Analysis Cross-section Analysis Engineering Analysis
What is Political analysis?
An analysis of the political situation.
What is achievement cluster and its characteristics?
An achievement cluster is a term used in entrepreneurship to describe the characteristics needed for entrepreneurs. These characteristics are divided into three clusters: achievement, planning, and power.The achievement cluster includes the following characteristics:Opportunity seekingCommitment to the work contractPersistenceRisk takingDemand for efficiency and quality
Concept of financial analysis?
concept of financial analysis?
What has the author E Backer written?
E. Backer has written: 'Computer-assisted reasoning in cluster analysis' -- subject(s): Expert systems (Computer science), Cluster analysis, Data processing
What has the author H Charles Romesburg written?
H. Charles Romesburg has written: 'Users manual for CLUSTAR/CLUSTID computer programs for hierarchical cluster analysis' -- subject(s): CLUSTAR, CLUSTID, Cluster analysis, Computer programs
What is the process of finding the difference when two groups are compared?
The answer depends on the context. One possible answer is cluster analysis.
When a scientist groups by similarities he or she is?
One answer might be that eh or she is doing cluster analysis. Please see the link.
What has the author M Ishaq Bhati written?
M. Ishaq Bhati has written: 'Cluster effects in mining complex data' -- subject(s): Cluster analysis, Econometrics, Data mining
What is the process of finding the difference when two groups are compared called?
The answer depends on the context. One possible answer is cluster analysis.
What has the author William B Warberg written?
William B. Warberg has written: 'An analysis of the ability and achievement of students in career cluster programs compared to students not in career cluster programs' -- subject(s): Vocational education
What has the author M Y Chia written?
M. Y. Chia has written: 'Cluster analysis to prioritise factors in microcomputer selection'
Where can someone find information about cluster analysis?
Cluster Analysis: An Overview Cluster analysis, also known as clustering, is a class of techniques used to classify objects or cases into relative groups called clusters. Objects in the same cluster are more alike to each other than those in different clusters. Purpose The primary goal of cluster analysis is to identify structures within data without providing any explicit information about these structures. It's essentially about discovering hidden patterns in the data. Types of Cluster Analysis Hierarchical Clustering: A method that starts by treating each observation as a separate cluster. Then, it repeatedly executes the following two steps: (1) identify the two clusters that are closest together, and (2) merge the two most similar clusters. This continues until all the clusters are merged together. K-Means Clustering: This method divides a dataset into 'K' number of clusters. The number K is specified in advance, and the method assigns each observation to the cluster that has the closest mean. DBSCAN (Density-Based Spatial Clustering of Applications with Noise): A density-based clustering algorithm that groups together points with many nearby neighbors. It's particularly useful when dealing with spatial data or when there's noise in your data. TwoStep Clustering: An algorithm often used for large datasets. It's a combination of hierarchical methods and partitioning methods. Applications Market Segmentation: Businesses can classify their customers into different segments or groups based on purchasing habits, interests, or behaviors. Biology: For classifying plants and animals based on their features. Social Network Analysis: To identify communities or groups of people with similar interests or behaviors. Document Clustering: Useful in web search where search results can be grouped into topics. Assessing Cluster Quality Internal Validation: Evaluate the clustering structure without reference to external information. Methods include silhouette coefficient, Davies-Bouldin index, etc. External Validation: Comparing the results of a cluster analysis to an externally known result, like class labels. Stability Analysis: Perturbing the data slightly and checking how much the clustering results change. Pros and Cons Pros: • Helps in uncovering hidden patterns in the data. • Does not require prior knowledge of group assignments. Cons: • The true number of clusters in the data might be unknown. • Sensitive to the scale of the data. • Different methods might yield different results on the same dataset. Conclusion Cluster analysis, while powerful, is both an art and a science. The choice of clustering method, as well as decisions regarding the number of clusters and the interpretation of results, often require domain expertise. Moreover, understanding the nature of the data, its distribution, and its scale is crucial before applying clustering techniques. If you found this analysis helpful, consider looking into other related methods like factor analysis and discriminant analysis for deeper dives into multivariate statistics and data classification. Additionally, you could watch this video, on Multivariate Analysis that tackles Cluster Analysis, on the YouTube channel of Charbel El Khouri, the video titled: "Marketing Research: Multivariate Analysis"
Where can someone find more information about Multivariate Analysis?
Multivariate analysis techniques enable researchers to analyze the relationships between multiple variables at once, providing more nuanced insights than univariate or bivariate methods. Some common multivariate techniques used in marketing research include: Multiple regression analysis Factor analysis Cluster analysis Discriminant analysis Conjoint analysis
What has the author Hermann Jahnke written?
Hermann Jahnke has written: 'Clusteranalyse als Verfahren der schliessenden Statistik' -- subject(s): Cluster analysis
What has the author John Kyoungyoon Park written?
John Kyoungyoon Park has written: 'Cluster analysis based on density estimates and its application to LANDSAT imagery'
