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How many approaches are for clustering?
These models can follow two approaches. In the first approach, they start with classifying all data points into separate clusters & then aggregating them as the distance decreases. In the second approach, all data points are classified as a single cluster and then partitioned as the distance increases.
Is agglomerative hierarchical clustering deterministic?
Hierarchical Agglomerative Clustering Using Common Neighbours Similarity. Hierarchical clustering algorithms are deterministic, stable, and do not need a pre-determined number of clusters as input. However, they are not scalable for very large data due to their non-linear complexity.
What is cluster and types of cluster?
Computer clusters can generally be categorized as three types: Highly available or fail-over. Load balancing. High performance computing.
When do you need a deterministic clustering method?
I need a deterministic [in the sense – robust to the ways of initial input / initial seeds] clustering method to group values in distributions that could be either random, normal or log-normal. Google mostly turns up k-means, which isn’t deterministic.
Which is more deterministic DBSCAN or hierarchical agglomerative clustering?
Hierarchical Agglomerative Clustering is deterministic except for tied distances when not using single-linkage. DBSCAN is deterministic, except for permutation of the data set in rare cases. k-means is deterministic except for initialization.
Which is family of clustering algorithms satisfies your conditions?
A family of clustering algorithms which satisfies your conditions is Spectral Clustering. They are batch algorithms and will give you the same results for the same datasets, even with different order. EDIT: also, there are some methods for deterministic initialization of K-Means clusters, such as this one.