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Mitigating the negative effect of intrabrand clustering: the role of interbrand clustering and firm size
Clustering—geographic concentrations of entities—has recently received more attention in marketing research and has been shown to affect multiple...
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Fuzzy clustering of financial time series based on volatility spillovers
In this paper we propose a framework for fuzzy clustering of time series based on directional volatility spillovers. In the case of financial time...
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A novel auto-pruned ensemble clustering via SOCP
Operations Research (OR) plays a crucial role in strategic decision-making in today’s business world; it uses complex algorithms and data analytic to...
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Mixed integer linear programming formulation for K-means clustering problem
The minimum sum-of-squares clusering is the most widely used clustering method. The minimum sum-of-squares clustering is usually solved by the...
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Tail dependence-based fuzzy clustering of financial time series
In this paper, we propose a new fuzzy clustering of time series with entropy regularization. Following a model-based approach, the dissimilarity...
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Data fusion algorithm of wireless sensor network based on clustering and fuzzy logic
In order to reduce network energy consumption and prolong the network lifetime in wireless sensor networks, a data fusion algorithm named CFLDF is...
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Clustering
This chapter will discuss the unsupervised machine learning technique known as clustering and its main approaches and use cases. After presenting...
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Robust asymmetric non-negative matrix factorization for clustering nodes in directed networks
Directed networks appear in an expanding array of applications, for example, the world wide web, social networks, transaction networks, and citation...
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An Improved Boosting Bald Eagle Search Algorithm with Improved African Vultures Optimization Algorithm for Data Clustering
Data clustering is one of the main issues in the optimization problem. It is the process of clustering a group of items into several groups. Items...
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Two-dimensional polygon classification and pairwise clustering for pairing in ship parts nesting
In the shipbuilding industry, nesting is arranging the cutting patterns of ship parts to increase the utilization rate of steel sheets and reduce the...
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Trust Cop-Kmeans Clustering Method
Social network large-scale decision-making (SNLSDM) has attracted widespread attention in the field of decision science. Clustering is one of the...
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Problem-driven scenario clustering in stochastic optimization
In stochastic optimisation, the large number of scenarios required to faithfully represent the underlying uncertainty is often a barrier to finding...
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Geometry-Inference Based Clustering Heuristic: New k-means Metric for Gaussian Data and Experimental Proof of Concept
K-means is one of the algorithms that are most utilized in data clustering; the number of metrics is coupled to k-means to reach reasonable levels of...
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Robust DTW-based entropy fuzzy clustering of time series
Time series are complex data objects whose partitioning into homogeneous groups is still a challenging task, especially in the presence of outliers...
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A typology of social innovation: A comparative study of clustering methodologies
This study offers a typology of the Social Innovation (SI) field. A sample of 5,152 documents from the Scopus database is screened using a clustering...
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Identifying household finance heterogeneity via deep clustering
Households are becoming increasingly heterogeneous. While previous studies have revealed many important insights (e.g., wealth effect, income...
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A fair-multicluster approach to clustering of categorical data
In the last few years, the need of preventing classification biases due to race, gender, social status, etc. has increased the interest in designing...
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Modulated spatiotemporal clustering of smart card users
Smart card data offers an in-depth understanding of the travel behavior of public transport users. An efficient way to analyze public transport users...
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Fuzzy clustering with entropy regularization for interval-valued data with an application to scientific journal citations
In recent years, the research of statistical methods to analyze complex structures of data has increased. In particular, a lot of attention has been...
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Cub model-based clustering of Likert-type data with a tourist satisfaction application
In investigating customer satisfaction with products or services, the most popular approach still relies on interviews or questionnaires to obtain...
