Analytical Comparison of Clustering Techniques for the Recognition of Communication Patterns

Kaya, Muhammed-Fatih; Schoop, Mareike

doi:10.1007/s10726-021-09758-7

Cited by 8 publications

(6 citation statements)

References 110 publications

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“…Jun and Lee (2010) made use of objective selection criteria in which the algorithms are compared with each other by their silhouette score (Jun and Lee, 2010). Kaya and Schoop (2022) have discussed the research approach in which they mentioned how the clustering algorithm can be chosen objectively with the help of Silhouette index, Davies-Bouldin index etc.…”

Section: Choosing Clustering Methodsmentioning

confidence: 99%

“…Rousseeuw (1987) introduced silhouette index. Silhouette Score is complicated with respect to running time but at the same time it provides better information than elbow and Davies-Bouldin score (Kaya and Schoop, 2022). This index lies between -1 to 1.…”

Section: Choosing Clustering Methodsmentioning

confidence: 99%

“…Authors (Kaya and Schoop, 2022;Xu and Wunsch II, 2005) have discussed a very systematic way of Clustering exercise as shown in figure which consists of following major steps: Feature selection selects distinctive characteristics from a group of candidates, whereas feature extraction employs modifications to produce usable and unique features out from originals (Bishop, 1995;Jain et al, 1999Jain et al, , 2000Xu and Wunsch II, 2005). Then Selection of clustering technique among many available can be be done with the help of cluster validity metrices.…”

Section: Systematic Clustering Proceduresmentioning

confidence: 99%

See 2 more Smart Citations

A Data-driven Approach for Planning Stock Keeping Unit (SKU) in a Steel Supply Chain

Wakle,

Toshniwal,

Jain

et al. 2024

Int. j. math. eng. manag. sci.

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In response to the growing complexities in supply chain management, there is an imperative need for a data-driven methodology aimed at optimizing inventory allocation strategies. The purpose of this research is to enhance the efficiency of allocation and operational scheduling, particularly concerning the stock keeping units (SKUs). To achieve this, one year's operational data from a specific organization's SKUs is taken and machine learning tools are employed on the data collected. These tools are instrumental in identifying clusters of SKUs that exhibit similar behaviour. Consequently, this research offers recommendations for rational inventory allocation strategies that are finely attuned to the unique characteristics of each SKU cluster. Results obtained reveals substantial disparities between the recommended strategies for the organization's SKUs and those typically found in the literature such as same strategy cannot be used for all different types for products. This underscores the critical importance of adopting a tailored approach to supply chain management. Furthermore, the research demonstrates the remarkable efficiency of unsupervised machine learning algorithms in determining the optimal number of segments within the SKUs. The current research differentiates from others in a way that in most of the research, the holistic data-driven approach is underutilized, right from the selection of the clustering algorithm to the validation of segments.

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Section: Choosing Clustering Methodsmentioning

confidence: 99%

Section: Choosing Clustering Methodsmentioning

confidence: 99%

Section: Systematic Clustering Proceduresmentioning

confidence: 99%

See 1 more Smart Citation

A Data-driven Approach for Planning Stock Keeping Unit (SKU) in a Steel Supply Chain

Wakle,

Toshniwal,

Jain

et al. 2024

Int. j. math. eng. manag. sci.

View full text Add to dashboard Cite

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“…The term "curse of dimensionality" [24,25] is a well-known one used to describe the issue. As a result, it has been discovered that numerous traditional anomaly detection methods [26][27][28] are inappropriate for high-dimensional data because they lose their effectiveness. In [29], the authors suggested a method for high-dimensional and categorical data anomaly detection.…”

Section: Introduction 1origin Of the Problemmentioning

confidence: 99%

Real-Time Anomaly Detection with Subspace Periodic Clustering Approach

Mazarbhuiya

Sahmoudi

2023

Applied Sciences

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Finding real-time anomalies in any network system is recognized as one of the most challenging studies in the field of information security. It has so many applications, such as IoT and Stock Markets. In any IoT system, the data generated is real-time and temporal in nature. Due to the extreme exposure to the Internet and interconnectivity of the devices, such systems often face problems such as fraud, anomalies, intrusions, etc. Discovering anomalies in such a domain can be interesting. Clustering and rough set theory have been tried in many cases. Considering the time stamp associated with the data, time-dependent patterns including periodic clusters can be generated, which could be helpful for the efficient detection of anomalies by providing a more in-depth analysis of the system. Another issue related to the aforesaid data is its high dimensionality. In this paper, all the issues related to anomaly detection are addressed, and a clustering-based approach is proposed for finding real-time anomalies. The method employs rough set theory, a dynamic k-means clustering algorithm, and an interval superimposition approach for finding periodic, partially periodic, and fuzzy periodic clusters in the subspace of the dataset. The data instances are thought to be anomalous if they either belong to sparse clusters or do not belong to any clusters. The efficacy of the method can be assessed by means of both time-complexity analysis and comparative studies with existing clustering-based anomaly detection algorithms on a synthetic and a real-life dataset. It can be found experimentally that our method outperforms others and runs in cubic time.

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“…One of the most active research areas is data clustering, among others [7][8][9]. Data clustering techniques can be used to perform similarity searches, pattern recognition, trend analysis, grouping, and classification, among other tasks [3,[10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Accelerated univariate sequential clustering

Enayati

Mortazavi

Basiri

2022

Preprint

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Data clustering is an important task in the field of data mining. In many real applications, clustering algorithms must consider the order of data, resulting in the sequential clustering problem. For instance, analyzing the moving pattern of an object and detecting community structure in a complex network are related to sequential clustering. The constraint of the continuous region prevents previous clustering algorithms from being directly applied to the problem. A dynamic programming algorithm was proposed to address the issue, which returns the optimal sequential clustering. However, it is not scalable. This paper addresses the issue via a greedy stopping condition that prevents the algorithm from continuing to search when it's likely that the best solution has been found. Experimental results on multiple datasets show that the algorithm is much faster than its original solution while the optimality gap is negligible.

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Analytical Comparison of Clustering Techniques for the Recognition of Communication Patterns

Cited by 8 publications

References 110 publications

A Data-driven Approach for Planning Stock Keeping Unit (SKU) in a Steel Supply Chain

A Data-driven Approach for Planning Stock Keeping Unit (SKU) in a Steel Supply Chain

Real-Time Anomaly Detection with Subspace Periodic Clustering Approach

Accelerated univariate sequential clustering

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