Local search methods for k-means with outliers

Gupta, Shalmoli; Kumar, Ravi; Lu, Kefu; Moseley, Benjamin; Vassilvitskii, Sergei

doi:10.14778/3067421.3067425

Cited by 87 publications

(56 citation statements)

References 29 publications

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“…The sample time and the speed values of the road sections are adopted to 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 Time (h) perform the experiments. For comparison, K-means [14] and Fuzzy C-means methods [15] are also employed to perform the experiments. As the peak detection methods [25,26] are commonly used to detect the traffic peak periods.…”

Section: Methodsmentioning

confidence: 99%

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Traffic Peak Period Detection from an Image Processing View

Xiao

Wang

et al. 2018

Journal of Advanced Transportation

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Traffic peak period detection is very important for the guidance and control of traffic flow. Most common methods for traffic peak period detection are based on data analysis. They have achieved good performance. However, the detection processes are not intuitional enough. Besides that, the accuracy of these methods needs to be improved further. From an image processing view, we introduce a concept in corner detection, sharpness, to detect the traffic peak periods in this paper. The proposed method takes the traffic peak period detection problem as a salient point detection problem and uses the image processing strategies to solve this problem. Firstly, it generates a speed curve image with the speed data. With this image, the method for detection of salient points is adopted to obtain the peak point candidates. If one candidate has the lowest speed value, this candidate is the peak point. Finally, the peak period is gotten by moving forward and backward the corresponding time of the peak point with a time interval. Experimental results show that the proposed method has achieved higher accuracy. More importantly, as the proposed method solves the traffic peak period detection problem from an image processing view, it has more intuition.

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Section: Methodsmentioning

confidence: 99%

“…K-means [14] and Fuzzy C-means methods [15] are the most common methods used in TPPD. Clustering methods have achieved good performances.…”

Section: Introductionmentioning

confidence: 99%

Traffic Peak Period Detection from an Image Processing View

Xiao

Wang

et al. 2018

Journal of Advanced Transportation

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“…ANN is used frequently in STLF with good/satisfactory performance. It can model an unspecified nonlinear relationship between load and weather variables [35].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…One of the well‐known clustering algorithms that is widely studied is K‐means . It is a simple method to partition data into specified number of clusters (k).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid clustering and classification technique for forecasting short‐term energy consumption

Torabi

Hashemi

Saybani

et al. 2018

Env Prog and Sustain Energy

122

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This paper presents a hybrid approach to predict the electric energy usage of weather‐sensitive loads. The presented method utilizes the clustering paradigm along with ANN and SVM approaches for accurate short‐term prediction of electric energy usage, using weather data. Since the methodology being invoked in this research is based on CRISP data mining, data preparation has received a great deal of attention in this research. Once data pre‐processing was done, the underlying pattern of electric energy consumption was extracted by the means of machine learning methods to precisely forecast short‐term energy consumption. The proposed approach (CBA‐ANN‐SVM) was applied to real load data and resulting higher accuracy comparing to the existing models. © 2018 American Institute of Chemical Engineers Environ Prog, 38: 66–76, 2019

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“…There have been increasing eorts in recent years to study clustering problems from both a theoretical and practical point of view [6,8,11,12].…”

Section: Introductionmentioning

confidence: 99%

Fully Dynamick-Center Clustering

Chan

Guerqin

Sozio

2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

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Static and dynamic clustering algorithms are a fundamental tool in any machine learning library. Most of the eorts in developing dynamic machine learning and data mining algorithms have been focusing on the sliding window model (where at any given point in time only the most recent data items are retained) or more simplistic models. However, in many real-world applications one might need to deal with arbitrary deletions and insertions. For example, one might need to remove data items that are not necessarily the oldest ones, because they have been agged as containing inappropriate content or due to privacy concerns. Clustering trajectory data might also require to deal with more general update operations.We develop a (2 + )-approximation algorithm for the k-center clustering problem with "small" amortized cost under the fully dynamic adversarial model. In such a model, points can be added or removed arbitrarily, provided that the adversary does not have access to the random choices of our algorithm. The amortized cost of our algorithm is poly-logarithmic when the ratio between the maximum and minimum distance between any two points in input is bounded by a polynomial, while k and are constant. Our theoretical results are complemented with an extensive experimental evaluation on dynamic data from Twitter, Flickr, as well as trajectory data, demonstrating the eectiveness of our approach.

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Local search methods for k-means with outliers

Cited by 87 publications

References 29 publications

Traffic Peak Period Detection from an Image Processing View

Traffic Peak Period Detection from an Image Processing View

A Hybrid clustering and classification technique for forecasting short‐term energy consumption

Fully Dynamick-Center Clustering

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