What Are The Different Clustering In Mahout?

1.	What Are The Different Clustering In Mahout?
Answer» Mahout SUPPORTS several clustering-algorithm implementations, all written in Map-Reduce, each with its own set of goals and criteria: Canopy: A FAST clustering algorithm often used to create initial seeds for other clustering ALGORITHMS. k-Means (and FUZZY k-Means): Clusters items into k clusters based on the distance the items are from the centroid, or center, of the previous iteration. Mean-Shift: Algorithm that does not require any a PRIORI knowledge about the number of clusters and can produce arbitrarily shaped clusters. Dirichlet: Clusters based on the mixing of many probabilistic models giving it the advantage Mahout supports several clustering-algorithm implementations, all written in Map-Reduce, each with its own set of goals and criteria:

Answer»

Mahout SUPPORTS several clustering-algorithm implementations, all written in Map-Reduce, each with its own set of goals and criteria:

Canopy: A FAST clustering algorithm often used to create initial seeds for other clustering ALGORITHMS.
k-Means (and FUZZY k-Means): Clusters items into k clusters based on the distance the items are from the centroid, or center, of the previous iteration.
Mean-Shift: Algorithm that does not require any a PRIORI knowledge about the number of clusters and can produce arbitrarily shaped clusters.
Dirichlet: Clusters based on the mixing of many probabilistic models giving it the advantage

Mahout supports several clustering-algorithm implementations, all written in Map-Reduce, each with its own set of goals and criteria:

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