An efficient Split-Merge re-start for the K-means algorithm

Capó, Marco; Pérez, Aritz; Lozano, José A.

doi:10.1109/tkde.2020.3002926

Cited by 24 publications

(13 citation statements)

References 30 publications

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“…Figure 2 is a block diagram of the system. e application system is based on the Telnet protocol; by intercepting the data packets transmitted in the network and performing corresponding protocol analysis, the commands submitted by the user to the Telnet host and the results returned by the host can be completely restored [15]. We combine each command submitted by the user with other attributes associated with it to form an audit record.…”

Section: Mining Principle and Basicmentioning

confidence: 99%

Computer User Behavior Anomaly Detection Based on K-Means Algorithm

Yang

Hao

Zhao

et al. 2022

Security and Communication Networks

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As an effective network protection method, computer user behavior anomaly detection can detect unknown attack behaviors. In order to detect user behavior anomalies more efficiently, the authors propose a computer user behavior anomaly detection model based on the K-means algorithm. According to the actual characteristics of single-user behavior, the algorithm uses sliding time window to define transactions and uses the first location strategy to mine behavior patterns. On this basis, the fault-tolerant mode is adopted to compare the current behavior mode with the normal behavior mode, and the anomaly detection results are obtained. Experiments show that using data mining technology, the association rules of user commands, and the mining of sequence patterns, it can effectively discover the user’s behavior pattern, and using sequence matching algorithms such as recursive correlation functions and calculating the similarity between the user’s historical pattern and the current pattern, it provides the possibility to accurately judge user behavior. The following conclusions are obtained through experiments: the model training time is short, the accuracy is high, and it has certain robustness.

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Section: Mining Principle and Basicmentioning

confidence: 99%

Computer User Behavior Anomaly Detection Based on K-Means Algorithm

Yang

Hao

Zhao

et al. 2022

Security and Communication Networks

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“…3) K-means [58] method is adopted to cluster the raw data and the mining data, respectively. 4) Two metrics are used to evaluate the clustering performance, which are Normalized Mutual Information (NMI) [59] and Homogeneity (h) [60].…”

Section: Mining Abilitymentioning

confidence: 99%

OPP-Miner: Order-preserving sequential pattern mining

Wu¹,

Hu²,

Li³

et al. 2022

Preprint

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A time series is a collection of measurements in chronological order. Discovering patterns from time series is useful in many domains, such as stock analysis, disease detection, and weather forecast. To discover patterns, existing methods often convert time series data into another form, such as nominal/symbolic format, to reduce dimensionality, which inevitably deviates the data values. Moreover, existing methods mainly neglect the order relationships between time series values. To tackle these issues, inspired by order-preserving matching, this paper proposes an Order-Preserving sequential Pattern (OPP) mining method, which represents patterns based on the order relationships of the time series data. An inherent advantage of such representation is that the trend of a time series can be represented by the relative order of the values underneath the time series data. To obtain frequent trends in time series, we propose the OPP-Miner algorithm to mine patterns with the same trend (sub-sequences with the same relative order). OPP-Miner employs the filtration and verification strategies to calculate the support and uses pattern fusion strategy to generate candidate patterns. To compress the result set, we also study finding the maximal OPPs. Experiments validate that OPP-Miner is not only efficient and scalable but can also discover similar subsequences in time series. In addition, case studies show that our algorithms have high utility in analyzing the COVID-19 epidemic by identifying critical trends and improve the clustering performance. The algorithms and data can be downloaded from https: //github.com/wuc567/Pattern-Mining/tree/master/OPP-Miner.

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“…The closer the

{s_i}

is to 1, the number of clustering classes is best. Yinyang‐k‐Means++ improves the k‐Means++ algorithm effectively using the upper and lower limits of the distance between the data point and the centre point and avoids redundant distance calculations [25, 26]. The traditional k‐Means clustering algorithm (Lloyd) is mainly divided into two steps.…”

Section: Algorithm Selection and Evaluation Analysismentioning

confidence: 99%

Multi‐level refined power system operation mode analysis: A data‐driven approach

Bai

Meng

et al. 2022

IET Generation Trans & Dist

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The high renewable penetration will cause the power system operation mode (PSOM) to change frequently. At present, the selection of PSOM mainly depends on the experience of relevant staff. However, selecting PSOM based on experience is difficult to comprehensively and elaborately analyze the possible PSOMs within a dispatching cycle, which inevitably invites troubles to the power system planning and dispatching. In this paper, a data‐driven analysis method is proposed, which integrates the techniques of preprocessing, dimensionality reduction, clustering, and data visualization of high‐dimensional data sets of power system operation. Simultaneously, qualitative and quantitative methods are used to analyze, evaluate the PSOM and their development laws. Finally, to verify the effectiveness of the proposed multi‐level refinement extraction method of PSOM, the normal power system operation mode (NPSOM), transition power system operation mode (TPSOM), extreme power system operation mode (EPSOM) are obtained and analyzed according to the real operation data of the urban power system. The abundant analysis results show that the proposed fine‐grained extraction method of PSOM is compelling, facilitating the power grid enterprise operation and planning.

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An efficient Split-Merge re-start for the K-means algorithm

Cited by 24 publications

References 30 publications

Computer User Behavior Anomaly Detection Based on K-Means Algorithm

Computer User Behavior Anomaly Detection Based on K-Means Algorithm

OPP-Miner: Order-preserving sequential pattern mining

Multi‐level refined power system operation mode analysis: A data‐driven approach

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