Clustering analysis has become a ubiquitous information retrieval tool in a wide range of domains, but a more automatic framework is still lacking. Though internal metrics are the key players towards a successful retrieval of clusters, their effectiveness on real-world datasets remains not fully understood, mainly beca…
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48 results for “automatic clustering”
A new clustering method estimates non-linear boundaries and automatically selects the number of clusters.
problem Discriminative clustering with non-linear boundaries and data abnormalities.
method Regularized mutual information objective function with a mixture of Gaussian and uniform distributions.
result Automatic selection of the number of components and estimation of non-linear boundaries.
A new method for automatically aligning and clustering time series data.
problem Challenges in aligning and clustering time series data, especially without a template signal.
method TROUT (Temporal Registration using Optimal Unitary Transformations) method based on a novel dissimilarity measure.
result TROUT outperforms competitors in clustering time series data.
Study EM and GD for clustering with penalties for misspecification and high dimensions.
problem Clustering with misspecification and high-dimensional data.
method Model-based Gaussian Mixture Models, EM algorithm, GD optimization with AD, penalized likelihood.
result GD outperforms EM on high-dimensional data but both have poor cluster interpretation.
CAF-HFCM automatically forms a cluster hierarchy and optimizes the number of clusters without trial-and-validation.
problem Challenges in determining the optimal number of clusters in fuzzy c-means.
method CAF-HFCM, an auto-fused hierarchical fuzzy c-means method.
result Automatic agglomeration and optimal number of clusters without validity indices.
A new clustering method uses nonparametric smoothing to estimate cluster membership functions.
problem Clustering with flexible, nonparametric estimation.
method Nonparametric smoothing to estimate cluster membership functions without explicit modelling assumptions.
result The method automatically determines the number of clusters and level of flexibility.
Abstraction is a fundamental part when learning behavioral models of systems. Usually the process of abstraction is manually defined by domain experts. This paper presents a method to perform automatic abstraction for network protocols. In particular a weakly supervised clustering algorithm is used to build an abstract…
We consider a problem of grouping multiple graphs into several clusters using singular value thesholding and non-negative factorization. We derive a model selection information criterion to estimate the number of clusters. We demonstrate our approach using "Swimmer data set" as well as simulated data set, and compare i…
Proposes a new model for clustering passenger trajectories with graphs.
problem Hierarchical trip structure, inaccurate clustering number, and lack of spatial semantic graphs.
method Tensor Dirichlet Process Multinomial Mixture model with graphs and a tensor version of Collapsed Gibbs Sampling.
result Automatic determination of the number of clusters and better cluster quality.
While computer and communication technologies have provided effective means to scale up many aspects of education, the submission and grading of assessments such as homework assignments and tests remains a weak link. In this paper, we study the problem of automatically grading the kinds of open response mathematical qu…
In this paper, a similarity-driven cluster merging method is proposed for unsuper-vised fuzzy clustering. The cluster merging method is used to resolve the problem of cluster validation. Starting with an overspecified number of clusters in the data, pairs of similar clusters are merged based on the proposed similarity-…
FOSC-X: An extended framework for extracting multiple optimal flat clusterings from hierarchical cluster trees
problem Extracting multiple optimal flat clusterings from hierarchical cluster trees
method Dynamic programming with lower and upper feasibility bounds
result Guaranteed optimal rankings of top-M solutions with linear-time complexity
This work uses image generation models to find vision model bugs.
problem Automatically discovering failures in vision models.
method Conditional text-to-image generation and captioning models.
result Demonstrated utility of large-scale generative models to find vision model bugs.
Intuitive clustering algorithm balances cluster size and cohesion.
problem Cluster definition and selection in data analysis.
method Nearest neighbours equilibrium condition for clustering.
result High-quality clustering solutions compared to benchmarks.
Proposes a new model for clustering passenger trips considering hierarchical and multi-dimensional data.
problem Clustering passenger trips with hierarchical and multi-dimensional data, especially in large-scale transportation systems.
method Tensor Dirichlet Process Multinomial Mixture (Tensor-DPMM) model, incorporating Dirichlet Process for automatic cluster number determination and tensor representation for multi-mode data.
result Automatic determination of the number of clusters and improved clustering quality.
We explore the performance of several automatic bandwidth selectors, originally designed for density gradient estimation, as data-based procedures for nonparametric, modal clustering. The key tool to obtain a clustering from density gradient estimators is the mean shift algorithm, which allows to obtain a partition not…
Spectral clustering is a popular and versatile clustering method based on a relaxation of the normalised graph cut objective. Despite its popularity, however, there is no single agreed upon method for tuning the important scaling parameter, nor for determining automatically the number of clusters to extract. Popular he…
We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step. Evolutionary clustering provides insight into cluster evolution and temporal changes in cluster memberships while enabling performance superior to that achieved by independently c…
Clustering analysis, a classical issue in data mining, is widely used in various research areas. This article aims at proposing a self-adaption grey DBSCAN clustering (SAG-DBSCAN) algorithm. First, the grey relational matrix is used to obtain the grey local density indicator, and then this indicator is applied to make …
We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the information given by adjacent time points to find the underlying cluster structure and obtain a smooth cluster evolution. This approach allows…
A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allow…
A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allow…
Efficiently clusters large datasets using low-density hyperplanes.
problem Clustering large datasets efficiently.
method Incremental estimation of low-density hyperplanes using stochastic gradient descent.
result The method automatically selects an appropriate number of clusters.
The goal of data clustering is to partition data points into groups to minimize a given objective function. While most existing clustering algorithms treat each data point as vector, in many applications each datum is not a vector but a point pattern or a set of points. Moreover, many existing clustering methods requir…
DPSM clusters nodes in data and graph spaces via density propagation and subcluster merging.
problem Automatic clustering of nodes in data and graph spaces.
method Density-based node clustering with propagation process and spectral clustering on subclusters.
result DPSM effectively clusters nodes in both data and graph spaces.
Gaussian Mixture Models (GMM) have found many applications in density estimation and data clustering. However, the model does not adapt well to curved and strongly nonlinear data. Recently there appeared an improvement called AcaGMM (Active curve axis Gaussian Mixture Model), which fits Gaussians along curves using an …
Efficient algorithms for clustered Lasso and OSCAR reduce computational costs.
problem High dimensional regression with feature clustering.
method Efficient path algorithms for clustered Lasso and OSCAR, reducing computational costs.
result Proposed algorithms are more efficient than existing methods in numerical experiments.
ParPIC clusters directed graphs using random walks and diffusion operators.
problem Challenges in vertex-level clustering for directed graphs due to edge directionality.
method Parametrized Power-Iteration Clustering (ParPIC) based on reversible random walks and diffusion operators.
result ParPIC achieves competitive clustering accuracy with improved scalability compared to spectral and teleportation-based methods.
A clustering method for multivariate populations with similar dependence structures.
problem Grouping populations with similar dependence structures.
method Orthogonal projection coefficients of density copulas estimated from populations.
result Clusters of populations with similar dependence structures.
A new hybrid fuzzy-crisp clustering algorithm addresses imbalanced cluster sizes.
problem Imbalanced influence in fuzzy c-means clustering for large vs. small clusters.
method Hybrid fuzzy-crisp algorithm combining linear and quadratic membership functions, setting exact zero membership for sufficiently distant points.
result The hybrid algorithm outperforms conventional fuzzy and crisp clustering methods on imbalanced datasets.
The determination of cluster centers generally depends on the scale that we use to analyze the data to be clustered. Inappropriate scale usually leads to unreasonable cluster centers and thus unreasonable results. In this study, we first consider the similarity of elements in the data as the connectivity of nodes in an…
In the travel industry, online customers book their travel itinerary according to several features, like cost and duration of the travel or the quality of amenities. To provide personalized recommendations for travel searches, an appropriate segmentation of customers is required. Clustering ensemble approaches were dev…
Multi-level conformal clustering: A distribution-free technique for clustering and anomaly detectionstat.ML
In this work we present a clustering technique called \textit{multi-level conformal clustering (MLCC)}. The technique is hierarchical in nature because it can be performed at multiple significance levels which yields greater insight into the data than performing it at just one level. We describe the theoretical underpi…
Unsupervised clustering of curves according to their shapes is an important problem with broad scientific applications. The existing model-based clustering techniques either rely on simple probability models (e.g., Gaussian) that are not generally valid for shape analysis or assume the number of clusters. We develop an…
Important information concerning a multivariate data set, such as clusters and modal regions, is contained in the derivatives of the probability density function. Despite this importance, nonparametric estimation of higher order derivatives of the density functions have received only relatively scant attention. Kernel …
Unified framework for clustering with auxiliary data.
problem Clustering with datasets reflecting similar but different latent structures.
method Adaptive Transfer Clustering (ATC) algorithm that optimizes bias-variance decomposition.
result ATC proves optimal under Gaussian mixture model and shows transfer benefits.
In this paper we propose a mixture model, SparseMix, for clustering of sparse high dimensional binary data, which connects model-based with centroid-based clustering. Every group is described by a representative and a probability distribution modeling dispersion from this representative. In contrast to classical mixtur…
A new medoid-based Silhouette method selects optimal cluster numbers efficiently.
problem Difficulty in evaluating clustering results and choosing the right number of clusters.
method Developed a medoid-based Silhouette variant, analyzed its properties, and provided fast optimization methods.
result A 10464x speedup in choosing optimal cluster numbers compared to original PAMMEDSIL.
VampPrior Mixture Model improves clustering in DLVMs.
problem Simplicity of standard priors in DLVMs leads to poor clustering performance.
method Leverages VampPrior concepts to fit a Bayesian GMM prior in a VAE.
result VMM achieves highly competitive clustering performance on benchmark datasets.
Recently, deep clustering, which is able to perform feature learning that favors clustering tasks via deep neural networks, has achieved remarkable performance in image clustering applications. However, the existing deep clustering algorithms generally need the number of clusters in advance, which is usually unknown in…
Data analysis in high-dimensional spaces aims at obtaining a synthetic description of a data set, revealing its main structure and its salient features. We here introduce an approach providing this description in the form of a topography of the data, namely a human-readable chart of the probability density from which t…
Neural clustering learns time series affinity from statistical features.
problem Challenging time series clustering with unknown cluster shapes and structures.
method Amortized neural inference using statistical features.
result Competitive clustering accuracy without manual specification of cluster shapes.
EGMM improves clustering by better handling uncertainty with evidential framework.
problem Clustering uncertainty and complexity in data.
method Proposes EGMM, a new model-based clustering algorithm using belief functions and EM algorithm.
result EGMM generates more informative evidential partitions and outperforms other algorithms.
Nowadays, data are generated massively and rapidly from scientific fields as bioinformatics, neuroscience and astronomy to business and engineering fields. Cluster analysis, as one of the major data analysis tools, is therefore more significant than ever. We propose in this work an effective Semi-supervised Divisive Cl…
We present an approach to model-based hierarchical clustering by formulating an objective function based on a Bayesian analysis. This model organizes the data into a cluster hierarchy while specifying a complex feature-set partitioning that is a key component of our model. Features can have either a unique distribution…
We introduce a semi-supervised discrete choice model to calibrate discrete choice models when relatively few requests have both choice sets and stated preferences but the majority only have the choice sets. Two classic semi-supervised learning algorithms, the expectation maximization algorithm and the cluster-and-label…
In this paper, we propose a semi-supervised clustering method, CEC-IB, that models data with a set of Gaussian distributions and that retrieves clusters based on a partial labeling provided by the user (partition-level side information). By combining the ideas from cross-entropy clustering (CEC) with those from the inf…
This paper focuses on obtaining clustering information about a distribution from its i.i.d. samples. We develop theoretical results to understand and use clustering information contained in the eigenvectors of data adjacency matrices based on a radial kernel function with a sufficiently fast tail decay. In particular, …