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Description | |||||||||||||
Clustering and grouping algorithms that are graph-invariant and require no user intervention. | |||||||||||||
Synopsis | |||||||||||||
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Clustering Algorithms | |||||||||||||
Non-deterministic algorithms | |||||||||||||
The Chinese Whispers Algorithm. This is an adaptation of the algorithm described in: Biemann, C. (2006): Chinese Whispers - an Efficient Graph Clustering Algorithm and its Application to Natural Language Processing Problems. Proceedings of the HLT-NAACL-06 Workshops on Textgraphs-06, New York, USA http://wortschatz.uni-leipzig.de/~cbiemann/pub/2006/BiemannTextGraph06.pdf The adaptations to this algorithm are as follows:
Simplistically, the way it works is this: 1. Every node is assigned into its own unique cluster. 2. Sort the nodes into some random order. Each node joins the most popular cluster in its neighbourhood (where popularity is defined as the sum of the node weightings in that cluster). 3. Repeat step 2. until a fixed point is reached. Note that this algorithm is non-deterministic, and that for some graphs no fixed point may be reached (and the algorithm may oscillate between a few different graph clusterings). Chinese Whispers is O(number of edges). | |||||||||||||
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The actual Chinese Whispers algorithm. | |||||||||||||
Spatial Algorithms | |||||||||||||
This implements the algorithm called CLUSTER, from the paper: Bandyopadhyay, S. (2003): An automatic shape independent clustering technique. Pattern Recognition, vol. 37, pp. 33-45. Simplistically, it defines clusters as groups of nodes that are spatially located closer to each other than to nodes in other clusters. It utilises the concept of a /Relative Neighbour Graph/ [RNG] to determine the spatial structure of a set of two-dimensional data points. The adaptations to this algorithm are as follows:
The algorithm is renamed relativeNeighbourhood. Experimentally, it seems to work better with larger graphs (i.e. more nodes), since then Graphviz makes the apparent clusters more obvious. The actual algorithm is O(n^2), where n is the number of Nodes in the graph. | |||||||||||||
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The renamed CLUSTER algorithm. Attempts to cluster a graph by using the spatial locations used by Graphviz. | |||||||||||||
Graph Collapsing | |||||||||||||
Collapse the parts of a graph down to try and show a compressed overview of the whole graph. It may be possible to extend this to a clustering algorithm by collapsing low density regions into high density regions. | |||||||||||||
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A collapsed node contains a list of nodes that it represents. | |||||||||||||
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Collapse the cliques, cycles and chains in the graph down. Note that this doesn't work too well on undirected graphs, since every pair of nodes forms a K_2 subgraph. | |||||||||||||
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Use the given functions to determine which nodes to collapse. | |||||||||||||
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Use the given functions to determine which nodes to collapse, with a new label to represent the collapsed nodes. | |||||||||||||
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Return True if the collapsed graph is either a singleton node or else isomorphic to the original graph (i.e. not collapsed at all). | |||||||||||||
Produced by Haddock version 2.4.2 |