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An intelligent technique for pattern-based clustering of continuous-valued datasets
In this paper, a novel computationally intelligent technique for Pattern-Based clustering has been proposed. The proposed technique works in two...
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Individualized passenger travel pattern multi-clustering based on graph regularized tensor latent dirichlet allocation
Individual passenger travel patterns have significant value in understanding passenger’s behavior, such as learning the hidden clusters of locations,...
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Community detection based on improved user interaction degree, weighted quasi-local path-based similarity and frequent pattern mining
Community detection is a significant research area in social networks. Most methods use network topology, but combining it with user interactions...
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Joint learning of fuzzy embedded clustering and non-negative spectral clustering
Fuzzy k-means clustering is widely acknowledged for its remarkable performance in data clustering. However, its effectiveness must improve when...
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Clustering
This chapter introduces the basic concepts of the clustering, and two most commonly used clustering algorithms: hierarchical clustering and k-means...
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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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Expectation maximization clustering and sequential pattern mining based approach for detecting intrusive transactions in databases
Database security is pertinent to every organisation with the onset of increased traffic over large networks especially the internet and increase in...
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On Information Granulation via Data Filtering for Granular Computing-Based Pattern Recognition: A Graph Embedding Case Study
Granular Computing is a powerful information processing paradigm, particularly useful for the synthesis of pattern recognition systems in structured...
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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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Trust factor-based analysis of user behavior using sequential pattern mining for detecting intrusive transactions in databases
Organizations today are employing databases on a large scale to store data essential for their functioning. Malicious access and modifications of the...
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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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Clustering customer orders in a smart factory using sequential pattern mining
In a smart factory, setting a production plan, relocating production equipment, and producing small batches of various products in real-time at a low...
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Constrained clustering with weak label prior
Clustering is widely exploited in data mining. It has been proved that embedding weak label prior into clustering is effective to promote its...
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SSCNet: learning-based subspace clustering
Sparse subspace clustering (SSC), a seminal clustering method, has demonstrated remarkable performance by effectively solving the data sparsity...
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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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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 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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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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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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Fuzzy-based bee algorithm for machine learning and pattern recognition of computational data of nanofluid heat transfer
The CFD approach could waste a lot of time, effort, and cost for three-dimensional turbulent flow modeling. In the CFD method, any changes in the...
