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Introducing the Cosine Clustering Index (CCI): A Balanced Approach to Evaluating Deep Clustering
Amidst the surge of Big Data, deep clustering emerges as a pivotal technique in machine learning, necessitating robust and interpretable evaluation...
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Correlation Clustering
Given a set of objects and a pairwise similarity measure between them, the goal of correlation clustering is to partition the objects in a set of...
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Clustering
We have seen index structures that manifest as trees, hash tables, and graphs. In this chapter, we will introduce a fourth way of organizing data...
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Clustering
Clustering is an important machine learning task that aims to discover “sensible” or “natural” groups (clusters) within a given data. In contrast...
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Text Clustering
This chapter explains the text clustering process in detail along with examples and implementation of each step in Python. During the process, the...
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Clustering
This video will show you why clustering is the most daunting task for a data scientist and provides guidelines on how to cluster small to enormously...
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Clustering
Clustering is the process of dividing objects and entities into meaningful and logically related groups. In contrast with classification where we...
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Sub-trajectory clustering with deep reinforcement learning
Sub-trajectory clustering is a fundamental problem in many trajectory applications. Existing approaches usually divide the clustering procedure into...
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Density peak clustering using tensor network
We introduce a density-based clustering algorithm with tensor networks. In order to demonstrate its effectiveness, we apply it to various types of...
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A novel squirrel search clustering algorithm for text document clustering
The amount of digital data is increasing exponentially in the web or the internet. This digital data appears in different forms having different...
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Bayesian contiguity constrained clustering
Clustering is a well-known and studied problem, one of its variants, called contiguity-constrained clustering, accepts as a second input a graph used...
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Clustering of Image Covariance Matrixes on Lie Group Manifold
AbstractAn image clustering method based on covariance matrix and mean-shift algorithm on Lie group manifold is proposed. Firstly, according to the...
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A Point-Cluster-Partition Architecture for Weighted Clustering Ensemble
Clustering ensembles can obtain more superior final results by combining multiple different clustering results. The qualities of the points,...
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CCET: towards customized explanation of clustering
Classical clustering algorithms use all features to partition a dataset, making it difficult for users to understand the clustering results. Some...
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Density Ratio Peak Clustering
Clustering is an important means of obtaining hidden information, and is widely used in economics, biomedicine and other disciplines. Data imbalance...
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Clustering-based visualizations for diagnosing diseases on metagenomic data
Metagenomic data has recently become crucial for precision or personalized medicine. However, these data are often complex, challenging to observe...
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Using Decision Trees for Interpretable Supervised Clustering
In this paper, we address an issue of finding explainable clusters of class-uniform data in labeled datasets. The issue falls into the domain of...
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Clustering with Intelligent Techniques
Cluster analysis is a technique for grou** data and finding structures in data. The most common application of clustering methods is to partition a...
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Split incremental clustering algorithm of mixed data stream
Clustering has been recognized as one of the most prominent functions in data mining. It aims to partition a given set of elements into homogeneous...
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Discovery of contextual factors using clustering
The Knowledge Discovery and Data Mining (KDDM) is a growing field of study argued to be very useful in discovering knowledge hidden in large...
