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Found 19 papers, 15 papers with code
Medoid Silhouette clustering with automatic cluster number selection
We discuss the efficient medoid-based variant of the Silhouette, perform a theoretical analysis of its properties, provide two fast versions for the direct optimization, and discuss the use to choose the optimal number of clusters.
Sparse Partitioning Around Medoids
FastPAM recently introduced a speedup for large k to make it applicable for larger problems, but the method still has a runtime quadratic in N. In this chapter, we discuss a sparse and asymmetric variant of this problem, to be used for example on graph data such as road networks.
Data Aggregation for Hierarchical Clustering
Hierarchical Agglomerative Clustering (HAC) is likely the earliest and most flexible clustering method, because it can be used with many distances, similarities, and various linkage strategies.
LOSDD: Leave-Out Support Vector Data Description for Outlier Detection
Support Vector Machines have been successfully used for one-class classification (OCSVM, SVDD) when trained on clean data, but they work much worse on dirty data: outliers present in the training data tend to become support vectors, and are hence considered "normal".
Stop using the elbow criterion for k-means and how to choose the number of clusters instead
A major challenge when using k-means clustering often is how to choose the parameter k, the number of clusters.
Clustering by Direct Optimization of the Medoid Silhouette
One of the versions guarantees equal results to the original variant and provides a run speedup of $O(k^2)$.
On Projections to Linear Subspaces
The merit of projecting data onto linear subspaces is well known from, e. g., dimension reduction.
HACAM: Hierarchical Agglomerative Clustering Around Medoids - and its Limitations
Unfortunately, we also show that the requirement to produce a hierarchical result is a limiting factor to the cluster quality, as the optimum result for a particular number of clusters 𝑘 does not have to be consistent with the optimum result with 𝑘+1 clusters.
MESS: Manifold Embedding Motivated Super Sampling
Many approaches in the field of machine learning and data analysis rely on the assumption that the observed data lies on lower-dimensional manifolds.
Accelerating Spherical k-Means
Spherical k-means is a widely used clustering algorithm for sparse and high-dimensional data such as document vectors.
A Triangle Inequality for Cosine Similarity
In this paper, we derive a triangle inequality for Cosine similarity that is suitable for efficient similarity search with many standard search structures (such as the VP-tree, Cover-tree, and M-tree); show that this bound is tight and discuss fast approximations for it.
Fast and Eager k-Medoids Clustering: O(k) Runtime Improvement of the PAM, CLARA, and CLARANS Algorithms
While we do not study the parallelization of our approach in this work, it can easily be combined with earlier approaches to use PAM and CLARA on big data (some of which use PAM as a subroutine, hence can immediately benefit from these improvements), where the performance with high k becomes increasingly important.
ABID: Angle Based Intrinsic Dimensionality
In this paper we introduce an orthogonal concept, which does not use any distances: we use the distribution of angles between neighbor points.
BETULA: Numerically Stable CF-Trees for BIRCH Clustering
We introduce a replacement cluster feature that does not have this numeric problem, that is not much more expensive to maintain, and which makes many computations simpler and hence more efficient.
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"
We will first outline the motivation for this release, the plans for the future, and then give a brief overview over the new functionality in this version.
Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms
It can easily be combined with earlier approaches to use PAM and CLARA on big data (some of which use PAM as a subroutine, hence can immediately benefit from these improvements), where the performance with high k becomes increasingly important.
Improving the Cluster Structure Extracted from OPTICS Plots
Density-based clustering is closely associated with the two algorithms DBSCAN and OPTICS.
Semantic Word Clouds with Background Corpus Normalization and t-distributed Stochastic Neighbor Embedding
We then select keywords based on their significance and construct the word cloud based on the derived affinity.
A Framework for Clustering Uncertain Data
The challenges associated with handling uncertain data, in particular with querying and mining, are finding increasing attention in the research community.
