Papers Clustering Algorithms Evaluation
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CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series Clustering
Time series clustering promises to uncover hidden structural patterns in data with applications across healthcare, finance, industrial systems, and other critical domains. However, without validated ground truth informat…
ClusteringClustering Algorithms EvaluationClustering Multivariate Time SeriesTime Series+1Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm
This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). The analysis of such data has become more …
ClusteringClustering Algorithms EvaluationClustering Multivariate Time SeriesTime Series+1Git: Clustering Based on Graph of Intensity Topology
\textbf{A}ccuracy, \textbf{R}obustness to noises and scales, \textbf{I}nterpretability, \textbf{S}peed, and \textbf{E}asy to use (ARISE) are crucial requirements of a good clustering algorithm. However, achieving these g…
ClusteringClustering Algorithms EvaluationThe Area Under the ROC Curve as a Measure of Clustering Quality
The Area Under the the Receiver Operating Characteristics (ROC) Curve, referred to as AUC, is a well-known performance measure in the supervised learning domain. Due to its compelling features, it has been employed in a …
ClusteringClustering Algorithms EvaluationAn Internal Cluster Validity Index Using a Distance-based Separability Measure
To evaluate clustering results is a significant part of cluster analysis. There are no true class labels for clustering in typical unsupervised learning. Thus, a number of internal evaluations, which use predicted labels…
ClusteringClustering Algorithms EvaluationA predictive model for the identification of learning styles in MOOC environments
Massive online open course (MOOC) platform generates a large amount of data, which provides many opportunities for studying the behaviors of learners. In parallel, recent advancements in machine learning techniques and b…
Anomaly DetectionAutomatic Machine Learning Model SelectionClustering Algorithms EvaluationEvent data classificationThe SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering
When it comes to clustering nonconvex shapes, two paradigms are used to find the most suitable clustering: minimum cut and maximum density. The most popular algorithms incorporating these paradigms are Spectral Clusterin…
ClusteringClustering Algorithms EvaluationAn Internal Validity Index Based on Density-Involved Distance
It is crucial to evaluate the quality of clustering results in cluster analysis. Although many cluster validity indices (CVIs) have been proposed in the literature, they have some limitations when dealing with non-spheri…
ClusteringClustering Algorithms EvaluationClustering EnsembleFace Clustering+3Clubmark: a Parallel Isolation Framework for Benchmarking and Profiling Clustering Algorithms on NUMA Architectures
There is a great diversity of clustering and community detection algorithms, which are key components of many data analysis and exploration systems. To the best of our knowledge, however, there does not exist yet any uni…
BenchmarkingClusteringClustering Algorithms EvaluationCommunity Detection+2CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities
The problem of inhomogeneous cluster densities has been a long-standing issue for distance-based and density-based algorithms in clustering and anomaly detection. These algorithms implicitly assume that all clusters have…
Anomaly DetectionClusteringClustering Algorithms EvaluationScalable Matching and Clustering of Entities with FAMER
Entity resolution identifies semantically equivalent entities, e.g. describing the same product or customer. It is especially challenging for Big Data applications where large volumes of data from many sources have to be…
ClusteringClustering Algorithms EvaluationEntity ResolutionAn in-network data cleaning approach for wireless sensor networks
Wireless Sensor Networks (WSNs) are widely used for monitoring physical happenings of the environment. However, the data gathered by the WSNs may be inaccurate and unreliable due to power exhaustion, noise and other re…
Clustering Algorithms Evaluation