Applying subclustering and Lp distance in Weighted K-Means with distributed centroids

Amorim, Renato Cordeiro de; Makarenkov, Vladimir

doi:10.1016/j.neucom.2015.08.018

Cited by 33 publications

(16 citation statements)

References 21 publications

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“…The noise model 1 addresses the issue of generic clustering methods based on the least-squares criterion (2): they cannot distinguish between useful and inadequate features. It has been used in [11,8,9,10] to test the weighted feature versions of k-means and Ward algorithms; those showed good cluster recovery properties against such noise features. The noise model 2 is novel.…”

Section: Comparing Ward and A-wardmentioning

confidence: 99%

“…Analogously to our previous simulation studies [10,8], we first found a set of partitions, each corresponding to a different combination of values of p and β. The set of all possible values of p and β was modelled using a grid of p and β values varying from 1.1 to 5.0 with the step of 0.1, as in [11].…”

Section: Validation Of the A-ward Pβ Algorithmmentioning

confidence: 99%

“…3.4. Estimating the optimal values of the exponents p and β To find good values for p and β in an unsupervised situation, we opted for the Silhouette width cluster validity index [33] which proved successful in the literature [1] as well as in our previous experiments [10,8,9]. The Silhouette width of a partition S is the average Silhouette width of entities y i ∈ Y , defined as follows:…”

Section: Validation Of the A-ward Pβ Algorithmmentioning

confidence: 99%

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A-Ward pβ : Effective hierarchical clustering using the Minkowski metric and a fast k -means initialisation

Amorim

Mirkin

2016

Information Sciences

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In this paper we make two novel contributions to hierarchical clustering. First, we introduce an anomalous pattern initialisation method for hierarchical clustering algorithms, called A-Ward, capable of substantially reducing the time they take to converge. This method generates an initial partition with a sufficiently large number of clusters. This allows the cluster merging process to start from this partition rather than from a trivial partition composed solely of singletons.Our second contribution is an extension of the Ward and Ward p algorithms to the situation where the feature weight exponent can differ from the exponent of the Minkowski distance. This new method, called A-Ward pβ , is able to generate a much wider variety of clustering solutions. We also demonstrate that its parameters can be estimated reasonably well by using a cluster validity index.We perform numerous experiments using data sets with two types of noise, insertion of noise features and blurring within-cluster values of some features. These experiments allow us to conclude: (i) our anomalous pattern initialisation method does indeed reduce the time a hierarchical clustering algorithm takes to complete, without negatively impacting its cluster recovery ability; (ii) AWard pβ provides better cluster recovery than both Ward and Ward p .

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Section: Comparing Ward and A-wardmentioning

confidence: 99%

Section: Validation Of the A-ward Pβ Algorithmmentioning

confidence: 99%

Section: Validation Of the A-ward Pβ Algorithmmentioning

confidence: 99%

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A-Ward pβ : Effective hierarchical clustering using the Minkowski metric and a fast k -means initialisation

Amorim

Mirkin

2016

Information Sciences

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“…The k-means algorithm runs inconvenient time if it is based on the histogram-based approaches [2,3]. A histogram shows the number of elements such that each has the same attributes.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the attributes of remote sensing image pixels for fast k-meansclustering

Sağlam¹,

Baykan²

2019

Turk J Elec Eng & Comp Sci

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Clustering process is an important stage for many data mining applications. In this process, data elements are grouped according to their similarities. One of the most known clustering algorithms is the k-means algorithm.The algorithm initially requires the number of clusters as a parameter and runs iteratively. Many remote sensing image processing applications usually need the clustering stage like many image processing applications. Remote sensing images provide more information about the environments with the development of the multispectral sensor and laser technologies. In the dataset used in this paper, the infrared (IR) and the digital surface maps (DSM) are also supplied besides the red (R), the green (G), and the blue (B) color values of the pixels. However, remote sensing images come with very large sizes (6000 × 6000 pixels for each image in the dataset used). Clustering these large-size images using their multiattributes consumes too much time if it is used directly. In the literature, some studies are available to accelerate the k-means algorithm. One of them is the normalized distance value (NDV)-based fast k-means algorithm that benefits from the speed of the histogram-based approach and uses the multiattributes of the pixels. In this paper, we evaluated the effects of these attributes on the correctness of the clustering process with different color space transformations and distance measurements. We give the success results as peak signal-to-noise ratio and structural similarity index values using two different types of reference data (the source images and the ground-truth images) separately. Finally, we give the results based on accuracy measurement for evaluating both the success of the clustering outputs and the reliability of the NDV-based measurement methods presented in this paper.

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“…The main goal of the clustering methods is to group the given data according to their similarities so that the elements of any group will be similar entities (de Amorim and Makarenkov, 2016). To solve many practical problems, such as image segmentation, the clustering methods are commonly used in image processing applications.…”

Section: Introductionmentioning

confidence: 99%

Fast K-Means Color Image Clustering With Normalized Distance Values

Baykan

Sağlam

2018

Scitech

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ABSTRACT:Image segmentation is an intermediate image processing stage in which the pixels of the image are grouped into clusters such that the data resulted from this stage is more meaningful for the next stage. Many clustering methods are used widely to segment the images. For this purpose, most clustering methods use the features of the image pixels. While some clustering method consider the local features of images by taking into account the neighborhood system of the pixels, some consider the global features of images. The algorithm of the K-means clustering method, that is easy to understand and simple to put into practice, performs by considering the global features of the entire image. In this algorithm, the number of cluster is given by users initially as an input value. For the segmentation process, if the distribution of the pixels over a histogram is used, the algorithm runs faster. The values in the histogram must be discrete in a certain range. In this paper, we use the Euclidean distance between the color values of the pixels and the mean color values of the entire image for taking advantage of the every color values of the pixels. To obtain a histogram that consists of discrete values, we normalize the distance value in a specific range and round the values to the nearest integers for discretization. We tested the versions of K-means with the gray-level histogram and the distance value histogram on an urban image dataset getting from ISPRS WG III/4 2D Semantic Labeling dataset. Comparing the two histograms, the distance value histogram proposed in this paper is better than the gray-level histogram. Key Words: Clustering, Histogram, Image segmentation, K-meansNormalize Edilmiş Uzaklık Değerleri ile Hızlı K-ortalama Renkli Görüntü Segmentasyonu ÖZ: Görüntü segmentasyonu, görüntü piksellerinin kümelere gruplandığı orta seviye bir görüntü işleme aşamasıdır. Öyle ki, bu şamadan elde edilen veri daha sonraki aşamalar için önceki veriye göre daha anlamlı hale gelmiş olur. Birçok kümeleme metodu, görüntü segmentasyonu amacıyla yaygın bir şekilde kullanılmaktadır. Bu amaçla, çoğu kümeleme metodu görüntü piksellerinin özelliklerini kullanmaktadır. Bazı kümeleme metotları piksellerin komşuluk sistemini kullanarak görüntünün yerel özelliklerini ele alırken, bazıları da görüntünün genel özelliklerini ele almaktadır. Anlaşılması kolay ve uygulaması basit olan K-ortalama algoritması, bütün görüntünün özelliklerini ele alarak segmentasyon yapmaktadır. Bu algoritmada, küme sayısı başlangıç giriş değeri olarak kullanıcı tarafından verilmektedir. Bu segmentasyon işlemi için, eğer piksellerin bir histogram üzerindeki dağılımı kullanılırsa algoritma daha hızlı çalışmaktadır. Bu histogram üzerindeki değerler belirli bir aralıkta ve ayrık olmak zorundadır. Bu çalışmada, piksellerin her bir renk değerinden faydalanmak için piksellerin renk değerleri ile görüntünün ortalama renk değerleri arasındaki Öklit uzaklığı kullanılmıştır. Ayrık değerlerden oluşan bir histogram elde etmek için, uzaklık değerlerini belirli değer ara...

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Applying subclustering and Lp distance in Weighted K-Means with distributed centroids

Cited by 33 publications

References 21 publications

A-Ward pβ : Effective hierarchical clustering using the Minkowski metric and a fast k -means initialisation

A-Ward pβ : Effective hierarchical clustering using the Minkowski metric and a fast k -means initialisation

Evaluating the attributes of remote sensing image pixels for fast k-meansclustering

Fast K-Means Color Image Clustering With Normalized Distance Values

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