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Cluster Analysis - Fallen Soldier
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- 1What is clustering?Finding groups of objects such that objects in one group will be similar to one another and different from objects in other groups
- 2Partitional clusteringData objects in non-overlapping subsets (clusters) such that each object is in exactly one subset.
- 3Hierarchical clusteringA set of nested clusters organized as a hierarchical tree
- 4Clustering algorithms (3 used in this course)K - means (partitional)
Density - based clustering
Hierarchical clustering - 5Clustering distinctions 1. Exclusive versus non-exclusive 2. Fuzzy versus non-fuzzy 3. Partial versus complete 4. Heterogeneous versus homogeneous1. non-exclusive: points may belong to multiple clusters
2. a point belongs to every cluster with weight between 0 and 1. weights must sum to 1
3. partial cluster: only want to cluster some of the data
4. clusters of widely different sizes, shapes and densities - 6Centroid(typically) The mean of the points in the cluster
- 7K - means complexityO(n*K*I*d)
n = number of points
K = number of clusters
I = number of iterations
d = number of attributes - 8Well-separated clusterEvery point in a cluster is closer to every other point in the cluster, than to any point not in the cluster
- 9Center-based clusterEvery point in the cluster is close to the center of the cluster
(often the centroid) - 10Contiguity- based clusterA point in a a cluster is closer to one or more other points in the cluster than to any point not in the cluster
- 11Density-based clusterA cluster is a dense region of points separated by low-density regions from other regions of high density
- 12Shared property clusters (conceptual clusters)Clusters that share some common property or represent a particular concept
- 13Pre-processing and post-processing (clustering)Pre-processing:
normalize the data and eliminate outliers
Post-processing:
1. eliminate small clusters that may represent outliers.
2.Split loose clusters with high SSE.
3.Merge clusters with low SSE. - 14Strengths of hierarchical clustering* Desired number of clusters can be obtained by cutting the dendogram.
* Meaningful taxonomies e.g. (animal kingdom) - 15Two main types of hierarchical clusteringAgglomerative
Divisive - 16Cluster similarity - MIN or Single LinkSimilarity of two clusters is based on the two most similar (closest) points in the different clusters
- 17Cluster similarity - MAX or Complete LinkSimilarity of two clusters is based on the two most different (distant) points in the different clusters
- 18Cluster similarity - Group averageProximity of two clusters is the average of pairwise proximity between points in two clusters
- 19Cluster similarity - Ward's methodSimilarity of two clusters is based on the increase in squared error when two clusters are merged. Less susceptible to noise.
- 20DBSCAN - densityDensity = the number of points within a specified radius Eps
- 21DBSCAN - MinPtsA point is a core point if it has more than a specified number of points (MinPts) within Eps
- 22DBSCAN - BorderA border point has fewer than MinPts within Eps, but is in the neighborhood of a core point.
- 23DBSCAN - NoiseA noise point is any point that is not a core point or a border point
- 24DBSCAN - pros and consPros: cluster of arbitrary shape, robust to noise, does not need a priori k
Cons: required connected regions of sufficiently high density, data sets with varying densities are problematic
Información
- Descripción
- Cluster analysis
- Creado por
- Tyson Mcleod · oct 2018
- Actualizado
- 24 oct 2018
- Visitas
- 18
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- Valoración