A Study on K -Means Clustering

Bae, Whasoo; Roh, Se-Won

doi:10.5351/ckss.2005.12.2.497

Cited by 7 publications

(2 citation statements)

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“…Using the distances between k cluster centers and n observations, we assign the observations to nearby cluster centers, repeat calculations of cluster centers using assigned observations, and minimize the distance between all observations and allocated cluster centers. This method caters to most of data forms and is easy to apply as no special transformations are necessary, but it needs to preset the number of clusters, k (Bae and Roh, 2005).…”

Section: Analysis Methodsmentioning

confidence: 99%

Verifing Formation of Area of Influence of Subway Station through Land Value Distribution Analysis - Case Study on Seoul

Lee¹,

Lee²

2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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This research has the purpose to develop a method to evaluate whether station's area of influence has been formed, and verify formation of the area of influence through empirical analysis of all subway stations in Seoul. First, we created buffers of 100m intervals from 100m to 1000m, based on subway station exits, calculated the average land price of each buffer, and divided station areas of influence into 10 clusters using K-means clustering with the average land prices as values of observation. Subsequently, we have assumed a decreasing price curve from increasing distance from a nearby subway station, estimated a price curve and evaluated whether the area of influence actually exists using regression analysis of each cluster. The 10 area of influence clusters were largely divided into strong, weak, and no area of influence of subway station. The stations where the strong areas of influence are formed are mainly located in center, sub-centers, and local centers; stations where weak and no areas of influence are formed are mostly located in the adjacent areas of center or sub-centers or suburbs. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Section: Analysis Methodsmentioning

confidence: 99%

Verifing Formation of Area of Influence of Subway Station through Land Value Distribution Analysis - Case Study on Seoul

Lee¹,

Lee²

2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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“…This technique must necessarily be provided with information about K, which means the pre-defined number of clusters, and the decision of the seeds and centers of the clusters tend to have a significant influence on the formation of these clusters, depending on the data type [25]. To solve this, we determined the optimal parameter K and performed the clustering by comparing the sum squared error (SSE), which means the sum of squares of the distances between the nodes according to K. Figure 7 shows an example of K-means clustering.…”

Section: Network Clusteringmentioning

confidence: 99%

The Development of a Data-Based Leakage Pinpoint Detection Technique for Water Distribution Systems

Kim

Choi

2023

Mathematics

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Leakage is one of the abnormal conditions in water distribution systems (WDSs). Real-time monitoring can be used to prevent or recover quickly from leakage. However, this is not enough: for improved leakage detection, a status diagnosis of the WDS must be performed together with this real-time monitoring, and numerous studies have been conducted on this. Furthermore, the existing proposed methodology only provides optimal sensor location and fast recognition. This paper proposes a technique that can quantitatively evaluate the volume of leakage along with leakage detection using deep learning technology. The hydraulic data (e.g., pressure, velocity, and flow) from the calibrated hydraulic model were used as training data and deep learning techniques were applied to conduct a simultaneous detection of leakage volume and location. We examined various scenarios regarding leakage volume and location for the data configuration of a simulated leakage accident. Furthermore, for optimal leakage detection performance, the detection performance according to the size of the network, the meter types of meters, the number of meters, and the locations of the meters were analyzed. This study is expected to be helpful in various aspects such as recovery and restoration decision making after leakage, because it simultaneously identifies the amount and location of the leakage.

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