Titlebook: Clustering Methods for Big Data Analytics; Techniques, Toolboxe Olfa Nasraoui,Chiheb-Eddine Ben N‘Cir Book 2019 Springer Nature Switzerland

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Clustering Blockchain Data,of these datasets has diverse applications, such as detecting fraud and illegal transactions, characterizing major services, identifying financial hotspots, and characterizing usage and performance characteristics of large peer-to-peer consensus-based systems. Unsupervised learning methods in genera

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An Introduction to Deep Clustering,ing classification tasks which are considered supervised learning. Deep learning has also been widely used to learn richer and better data representations from big data, without relying too much on human engineered features. Even though it started mostly within the realm of supervised learning, deep

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Spark-Based Design of Clustering Using Particle Swarm Optimization,effective solution for Big data. However, MapReduce is unsuitable for iterative algorithms since it requires repeated times of reading and writing to disks. In addition, PSO suffers from a low convergence speed when it approaches the global optimum region. To deal with these issues, we propose in th

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Data Stream Clustering for Real-Time Anomaly Detection: An Application to Insider Threats,rs. The continuous streaming of unbounded data coming from various sources in an organisation, typically in a high velocity, leads to a typical Big Data computational problem. The malicious insider threat refers to anomalous behaviour(s) (outliers) that deviate from the normal baseline of a data str

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Effective Tensor-Based Data Clustering Through Sub-Tensor Impact Graphs,-based algorithms, most notably tensor decomposition, are becoming a core tool for data analysis and knowledge discovery, including clustering. Intuitively, tensor decomposition process generalizes matrix decomposition to high-dimensional arrays (known as tensors) and rewrites the given tensor in th

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Clustering Blockchain Data,s tailored to the characteristics of such data. This chapter motivates the study of clustering methods for blockchain data, and introduces the key blockchain concepts from a data-centric perspective. It presents different models and methods used for clustering blockchain data, and describes the chal

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