protected:agglomerative
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protected:agglomerative [2009/05/12 03:49] – mark | protected:agglomerative [2010/05/06 14:14] (current) – bil | ||
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====== Agglomerative Algorithms ====== | ====== Agglomerative Algorithms ====== | ||
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====== History ====== | ====== History ====== | ||
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====== Algorithm Intent ====== | ====== Algorithm Intent ====== | ||
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====== Factbase Properties ====== | ====== Factbase Properties ====== | ||
- | Factbased | + | |
+ | The factbase | ||
====== Clustering Objectives ====== | ====== Clustering Objectives ====== | ||
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====== Process Description ====== | ====== Process Description ====== | ||
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+ | Agglomerative algorithms start at the bottom of the hierarchy by iteratively grouping similar entities into clusters. At each step, the two clusters that are most similar to each other are merged and the number of clusters is reduced by one. | ||
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====== Decomposition Properties ====== | ====== Decomposition Properties ====== | ||
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+ | Complete linkage gives the most cohesive clusters. Clusters formed using single linkage approach are not cohesive as complete linkage, and the results of average linkage algorithm lie somewhere between those of single and complete linkage. | ||
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====== Algorithm Restrictions ====== | ====== Algorithm Restrictions ====== | ||
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====== Failed Assumptions ====== | ====== Failed Assumptions ====== | ||
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====== Detailed Algorithm Description ====== | ====== Detailed Algorithm Description ====== | ||
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+ | Agglomerative algorithms perform the following steps: | ||
+ | - Compute a similarity matrix | ||
+ | - Find the two most similar clusters and join them | ||
+ | - Calculate the similarity between the joined clusters and others obtaining a reduced matrix | ||
+ | - Repeat from step 2 until two clusters are left | ||
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protected/agglomerative.1242100170.txt.gz · Last modified: 2009/05/12 03:49 by mark