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Found 7 papers, 0 papers with code
Improving cluster recovery with feature rescaling factors
The data preprocessing stage is crucial in clustering.
Identifying meaningful clusters in malware data
It does not favour more meaningful features over those that are less meaningful, an effect one should perhaps expect of the data pre-processing stage.
An efficient density-based clustering algorithm using reverse nearest neighbour
We also show that it is possible to estimate a good value for this parameter using a clustering validity index.
A-Ward_p\b{eta}: Effective hierarchical clustering using the Minkowski metric and a fast k -means initialisation
This allows the cluster merging process to start from this partition rather than from a trivial partition composed solely of singletons.
Recovering the number of clusters in data sets with noise features using feature rescaling factors
In this paper we introduce three methods for re-scaling data sets aiming at improving the likelihood of clustering validity indexes to return the true number of spherical Gaussian clusters with additional noise features.
A survey on feature weighting based K-Means algorithms
In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance.
