Cluster analysis and the identification of aggregations

Animal Behaviour

Volumn 61, Issue 2, 2001, Pages 481-488

  Strauss, Richard E ^a  

^a TEXAS TECH UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0034965558     PISSN: 00033472     EISSN: None     Source Type: Journal    
DOI: 10.1006/anbe.2000.1608     Document Type: Editorial

References (64)

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5. Probabilistic aspects in cluster analysis
6. Classification and clustering: Problems for the future
7. Flocking: Advantages and disadvantages
8. Finding natural clusters
9. Neural networks: A review from a statistical perspective
10. Measuring the influence of individual data points in a cluster analysis
11. The evolutionary advantages of group foraging
12. n spacings
13. Hierarchical clustering of sampled functions
14. Complexity relaxation of dynamic programming for cluster analysis
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17. Null models in cluster validation
18. A survey of constrained classification
19. Hierarchical classification
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21. Geometry for the selfish herd
22. Partitioning problems in cluster analysis: A review of mathematical programming approaches
23. An examination of procedures for determining the number of clusters in a data set
24. An investigation of nine procedures for detecting the structure in a data set
25. Estimation of a convex density contour in two dimensions
26. n -nearest neighbor algorithm for unimodal clusters
27. A criterion for determining the number of groups in a data set using sum-of-squares clustering
28. Comparing actual and random distributions of nearest neighbors
29. On the theory and construction of k-clusters
30. Foraging, timidity and shoal size in minnows and goldfish
31. Graph theoretic techniques for cluster analysis algorithms
32. Properties of Gabriel graphs relevant to geographic variation research and the clustering of points in the plane
33. Clustering validation: Results and implications for applied analyses
34. An examination of procedures for determining the number of clusters in a data set
35. A two-stage clustering algorithm with robust recovery characteristics
36. Visualizing interaction and sequential data in animal behaviour: Theory and application of cluster-analysis methods
37. The gap test: An optimal method for determining the number of natural classes in cluster analysis
38. Robust bivariate boxplots and visualization of multivariate data
39. Generalization of the gap test for the detection of multivariate outliers
40. Least-squares mapping using interpoint distances
41. Dynamic additive fuzzy clustering model
42. A survey of multidimensional medians
43. On the longest edge of the minimal spanning tree
44. Analysing animal social structure
45. A new approach to clustering
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47. A bootstrap testing procedure for investigating the number of subpopulations
48. A kth nearest neighbor clustering procedure
49. A test for spatial randomness based on k-NN distances
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