A Survey of Clustering Data Mining Techniques

By Pavel Berkhin. 2006

3,367 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, methodology): “ optimal grouping between the time series by applying a conventional clustering algorithm to the extracted feature vector. Again, a host of dierent clustering methods exist, an overview gives, e.g., Berkhin (2006). We use a mixture-model-based clustering algorithm called \Snob", which we discus”
Understanding Membership Inferences on Well-Generalized Learning Models (2018, methodology): “performance by querying enhancing records. For each dataset, we randomly sampled 5;000 records, selected 50 of them by record clustering, and tested them with the enhancing record selection algorithm [6]. If less than 10 enhancing records were selected, we ran the enhancing record optimization algor”
Learning Discrete Representations via Information Maximizing Self-Augmented Training (2017, methodology): “Clustering is widely applied to data-driven application domains (Berkhin, 2006), 1University of Tokyo, Japan 2Preferred Networks, Inc., Japan 3ATR Cognitive Mechanism Laboratories, Japan 4RIKEN, Japan.”
Binary Multi-View Clustering (2019, background): “DATA clustering has been a fundamental research topic in the computer vision and data mining communities [1], [2], [3], [4].”
Grey wolf optimizer: a review of recent variants and applications (2017, background): “Clustering is a common machine learning and data mining task where the goal is to divide data instances into a number of groups that have similar characteristics in some sense [4].”
Learning similarity metrics for event identification in social media (2010, methodology): “, K-means, EM [7]) on each subset, with exact similarities.”
Quantitative Data Cleaning for Large Databases (2008, methodology): “The standard fully-automated scheme for partitioning data statistically is called clustering, and there are a wide range of techniques in the literature on efficient and scalable clustering schemes [Berkhin, 2002].”
Data Mining for the Internet of Things: Literature Review and Challenges (2015, neutral): “The divisive clustering in contrast is a top-down strategy; it starts with a single cluster containing all data points and recursively splits that cluster into appropriate subclusters [57, 58].”
Segmentation and Reconstruction of Polyhedral Building Roofs From Aerial Lidar Point Clouds (2010, background): “density-based methods mostly work in the original data space [32], where the data are separated into different spatial regions.”
EXPOSURE: Finding Malicious Domains Using Passive DNS Analysis (2011, background): “Euclidean Distance is a popular distance measuring algorithm that is often used in data mining [14, 37, 43].”
Subspace clustering for high dimensional data: a review (2004, background): “One of the more recent and comprehensive surveys was published by Berhkin and includes a small section on subspace clustering [11].”
Mining Electronic Health Records: A Survey (2017, background): “Survey from Berkhin et al. [Berkhin 2006], concentrates on clustering algorithms from a data mining perspective.”
Access Pattern disclosure on Searchable Encryption: Ramification, Attack and Mitigation (2012, methodology): “We use agglomerative hierarchical clustering algorithm [2] with average distance.”
Online Learning: A Comprehensive Survey (2018, background): “ring. For batch learning settings, clustering is usually classied into following categories: partition based clustering, hierarchical clustering, density-based clustering, and grid-based clustering (Berkhin, 2006). For online settings (Aggarwal, 2013), partition-based and density-based clustering ha”