Hybridizes EM and K-Means for faster clustering.
problem Improving clustering performance and speed.
method Combines Expectation-Maximization and K-Means algorithms.
result The hybrid algorithm outperforms both EM and K-Means in execution time and clustering fitness.
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.
Hybrid method for clustering text and graph data.
problem Discover latent connections and content associations in mixed data.
method Joint Nonnegative Matrix Factorization (NMF) for text and graph clustering.
result Higher quality clustering results compared to methods using only content or structure.
New properties for density-based dissimilarity measures in hybrid clustering are proposed and evaluated.
problem Choosing the right dissimilarity measure for hybrid clustering.
method Six data-independent properties for density-based dissimilarity measures are proposed and evaluated.
result A new dissimilarity measure based on Kullback-Leibler information is introduced and shown to satisfy all proposed properties.
A hybrid neural network improves medical diagnostics of reactive arthritis.
problem Overlapping classes in medical diagnostics data.
method Hybrid clustering-classification neural network with cosine similarity measures and fuzzy reasoning.
result Efficiency confirmed through experiments on reactive arthritis diagnostics.
Develops a hybrid MtFA approach for high-dimensional data clustering.
problem Scalability issues in traditional MtFA estimation methods for high-dimensional data.
method Integrates profile likelihood method into EM framework for efficient parameter estimation.
result Demonstrates superior computational efficiency and clustering accuracy compared to existing methods.
A hybrid method clusters and characterizes cancer data efficiently.
problem Challenges in clustering high-dimensional biomedical data.
method Gaussian mixture with generalized factor analyzers for efficient estimation.
result Our approach outperforms existing methods with faster convergence and higher accuracy.
New method clusters multi-view data by squeezing hybrid knowledge.
problem Removal of redundant information and fusion of multi-view features.
method Low-rank subspace multi-view clustering with adaptive graph regularization.
result Our method outperforms state-of-the-art algorithms on multi-view benchmarks.
Study clusters bank customers using LSTM and DTW.
problem Efficiently segmenting bank customers for targeted offers.
method Encoder-decoder LSTM network and Dynamic Time Warping (DTW).
result Hybrid method yields more accurate clusters.
Here, we propose a clustering technique for general clustering problems including those that have non-convex clusters. For a given desired number of clusters K, we use three stages to find a clustering. The first stage uses a hybrid clustering technique to produce a series of clusterings of various sizes (randomly se…
The problem of Hybrid Linear Modeling (HLM) is to model and segment data using a mixture of affine subspaces. Different strategies have been proposed to solve this problem, however, rigorous analysis justifying their performance is missing. This paper suggests the Theoretical Spectral Curvature Clustering (TSCC) algori…
Hybrid clustering merges K-means and hierarchical methods for diverse group shapes.
problem Clustering homogeneous spherical groups in large datasets.
method First, K-means partitions the dataset into spherical groups. Then, hierarchical clustering merges these groups with a data-driven distance measure. result Hybrid approach reveals general-shaped groups in datasets.
Develops algorithms for constructing statistical industry classifications.
problem Creating accurate industry classifications for quantitative trading.
method Combines clustering algorithms with correlation analysis to improve fundamental industry classifications.
result Shows that clustering methods improve off-the-shelf industry classifications.
A new clustering method reduces time and memory usage for massive datasets.
problem Prohibitive computational cost and memory usage of clustering algorithms for massive datasets.
method Iterative hybridized threshold clustering (IHTC) that reduces data points into prototypes and applies clustering algorithms on them.
result IHTC reduces run time and memory usage of k-means and HAC while preserving their performance. A new clustering method combines Kalman filtering with K-Means for better results.
problem Improving clustering efficiency and accuracy in data mining.
method Proposes a new clustering algorithm (HKA-K) that integrates Kalman filtering and K-Means.
result HKA-K outperforms other hybrid meta-heuristic clustering approaches on UCI datasets.
Paper uses ANFIS to predict cryptocurrency prices.
problem Predicting cryptocurrency prices for seven days.
method Adaptive Network Based Fuzzy Inference System (ANFIS) with hybrid and backpropagation algorithms.
result The method can predict cryptocurrency prices in a short time.
HyDaP clusters mixed-type data efficiently.
problem Clustering data with mixed types (continuous and categorical).
method Two-step hybrid approach: density-based and partition-based for continuous variables, partition-based for mixed data.
result HyDaP outperforms existing methods in clustering electronic health records.
Hybrid model improves synthetic equity data generation.
problem Generating realistic synthetic financial time series.
method Discretized excess growth rates into states with Poisson jumps, estimating parameters directly.
result Framework achieved high pass rates for distributional and volatility clustering tests.
Hybrid approach combines topic and graph embeddings for legal document clustering.
problem Challenges in classifying legal texts due to domain-specific language and limited labeled data.
method Combines unsupervised topic and graph embeddings with a supervised model.
result Improves clustering quality over text-only or graph-only embeddings.
Hybrid engine analyzes news sentiment for markets in real-time.
problem Real-time market analysis of news sentiment.
method Three-way ensemble learning combining financial lexicon, adaptive TF-IDF clustering, and auto-calibrated weighting.
result Adaptive statistical clustering learner improves adaptability to market changes.
Scalable hybrid HMM with Gaussian Process for time-series data clustering.
problem Large number of parameters and long sequences in time-series data make HMM-GPSM training difficult.
method Stochastic Variational Inference (SVI) for long sequences and reparameterized random Fourier features (R-RFF) for large data points.
result Significant reduction in training time and improved hidden-state estimation accuracy.
A hybrid approach detects financial market regime switches using PCA and k-means.
problem Detecting regime switches in financial markets for trend forecasting.
method Dimensionality reduction with PCA and clustering with k-means.
result Trading strategies based on detected regimes show improved performance.
Efficiently clusters data with weak assumptions, robust to contamination.
problem General-shaped clustering under weak parametric assumptions with data contamination.
method Two-step hybrid robust clustering algorithm combining trimmed k-means and hierarchical agglomeration.
result Outperforms state-of-the-art methods in various applications.
New method clusters multiway data from noisy tensors.
problem Identifying multiway block structure from noisy tensors.
method Tensor block model, unified least-square estimation, sparse regularization.
result Achieves partition consistency and outperforms previous methods.
Hybrid model combines DEA and data mining for evaluating bank branch efficiency.
problem Evaluating efficiency of bank branches to identify areas needing improvement.
method DEA for efficiency estimation, clustering for identifying classes, SOM for data partitioning, polynomial model for classification.
result Identifies problematic areas and provides managerial insights for enhancing branch performance.
Machine learning predicts liquid water properties from cluster data.
problem Accuracy of bulk properties from machine-learned potentials is limited by training data.
method Local, atom-centred descriptors enable prediction of bulk properties from cluster data.
result Excellent agreement with experimental and theoretical counterparts of liquid water properties.
Hybrid diarization framework handles overlapped speech and long recordings.
problem Challenges in clustering-based and end-to-end neural diarization approaches.
method Proposes a hybrid framework combining clustering and end-to-end neural diarization.
result Significantly better performance on long recordings with overlapped speech.
A new k-means algorithm using cover trees accelerates clustering.
problem Efficiently clustering large datasets with k-means.
method Combining cover trees with upper and lower bounds.
result Significantly reduces distance computations and improves clustering performance.
A new method uncovers intrinsic data structures for unsupervised domain adaptation.
problem Learning domain-aligned features can damage intrinsic target discrimination.
method Structurally Regularized Deep Clustering (H-SRDC) integrating structural source regularization.
result H-SRDC outperforms existing methods in image classification and semantic segmentation.
Hybrid VAE improves movie recommendation accuracy.
problem Improving personalized recommendations in online marketplaces.
method Combining movie embeddings from a sibling VAE network with user ratings for movie recommendation.
result Empirical evidence shows VAE network benefits from incorporating movie embeddings.
Hybrid approach improves crude oil price forecasting using multi-scale data.
problem Forecasting crude oil prices with multi-scale data.
method Hybrid approach combining K-means, KPCA, and KELM.
result Hybrid approach outperforms traditional methods in both level and directional forecasting accuracy.
Unsupervised framework captures acquisition variability in structural connectomes.
problem Acquisition differences across sites, scanners, and protocols complicate structural connectome analysis.
method An unsupervised framework using architectural annealing to balance discrete and continuous latent variables.
result Architectural annealing produces stronger site learning than baseline models.
Augmented k-means improves clustering accuracy and efficiency.
problem Identifying homogeneous clusters in heterogeneous datasets.
method Hybrid of k-means and logistic regression, using cluster probabilities to refine cluster means.
result Augmented k-means outperforms standard k-means in accuracy and convergence speed.
The random cluster model is used to define an upper bound on a distance measure as a function of the number of data points to be classified and the expected value of the number of classes to form in a hybrid K-means and regression classification methodology, with the intent of detecting anomalies. Conditions are given …
A new hybrid MCMC method guides MCMC with tree-based clustering for faster and more efficient inference.
problem Slow convergence of MCMC methods in posterior inference for NRM mixture models.
method Tree-guided MCMC (tgMCMC) that combines MCMC's convergence guarantees with IBHC's efficiency.
result tgMCMC provides faster convergence and better performance compared to MCMC and IBHC alone.
Study evaluates clustering methods for Google Trends data.
problem Clustering high-dimensional, noisy time series data.
method Symbolic Aggregate Approximation (SAX), Enhanced SAX (eSAX), and Topological Data Analysis (TDA).
result TDA provides more balanced and meaningful groupings than SAX and eSAX.
Parameter-free clustering method using cluster catch digraphs (CCDs).
problem Finding the correct number of clusters in data without specifying a parameter.
method Hybrid of density-based and graph-based clustering methods using Ripley's K function.
result Minimum dominating sets of RK-CCDs estimate and distinguish clusters from noise.
Deep clustering outperforms conventional networks in singing voice separation.
problem Challenging music source separation tasks.
method Combining deep clustering and conventional networks for better performance.
result Hybrid network outperforms both components.
Two novel algorithms improve scalability and robustness of spectral clustering for large datasets.
problem Scalability and robustness of spectral clustering for large-scale datasets.
method Ultra-scalable spectral clustering (U-SPEC) and ultra-scalable ensemble clustering (U-SENC) algorithms.
result Robust and efficient clustering of ten-million-level datasets on a PC.
Error-robust multi-view clustering tackles noisy data across multiple sources.
problem Error in multi-view data degrades clustering performance.
method Blind clustering without error consideration is ineffective. Various approaches like sparsity, graph, subspace, and deep learning are reviewed.
result Error-robust multi-view clustering improves clustering accuracy even with corrupted data.
Paper forecasts stock correlations using a hybrid model combining graph neural networks and transformers.
problem Improving stock correlation forecasts for better portfolio management.
method Hybrid model combining Transformer and graph attention networks for forecasting residual deviations from historical data.
result The hybrid model reduces correlation forecasting error compared to rolling-window estimates.
Method learns big Gaussian Bayesian networks by clustering, estimating, and fusing subgraphs.
problem Learning structure of large Bayesian networks with many nodes and few samples.
method Partition-estimation-fusion (PEF) framework: hierarchical clustering, subgraph learning, edge fusion.
result PEF method improves structure learning accuracy by 20% or more, with up to two orders-of-magnitude faster running time.
Paper presents robust clustering methods for general mixture models.
problem Clustering with sub-Gaussian error assumptions often invalid in practice.
method Hybrid clustering with robust centroid estimate and data-driven initialization.
result Provably near-optimal mislabeling guarantees for general error distributions.
A new method for efficient kernel k-means clustering.
problem Inefficiency of kernel k-means due to memory scaling issues.
method Approximate strategy and parallelization for large-scale clustering.
result Improved efficiency and scalability of kernel k-means.
This study forecasts climate data in Chile using EOFs and machine learning models.
problem Predicting climatic variability in Chile for resource management and planning.
method Combines EOF decomposition, wavelet analysis, and neural networks for spatiotemporal forecasting.
result Improved accuracy in forecasting climate data through a hybrid ML approach.
This paper examines the implementation of a statistical arbitrage trading strategy based on co-integration relationships where we discover candidate portfolios using multiple factors rather than just price data. The portfolio selection methodologies include K-means clustering, graphical lasso and a combination of the t…
A new multi-view clustering method that is fast, scalable, and easy to use.
problem High computational complexity, one-stage fusion, and dataset-specific hyperparameter tuning in multi-view clustering.
method Random view groups, hybrid early-late fusion, diversified base clusterings, and unified bipartite graph.
result Almost linear time and space complexity, no dataset-specific tuning required.
We present a simple and fast geometric method for modeling data by a union of affine subspaces. The method begins by forming a collection of local best-fit affine subspaces, i.e., subspaces approximating the data in local neighborhoods. The correct sizes of the local neighborhoods are determined automatically by the Jo…