Careful Seeding Method based on Independent Components Analysis for k-means Clustering

Onoda, Takashi; Sakai, Miho; Yamada, Seiji

doi:10.4304/jetwi.4.1.51-59

Cited by 17 publications

(9 citation statements)

References 18 publications

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“…However, the KKZ method sometimes finds bad clusters because unfortunately it depends on outlier data points [8]. This method has one obvious pitfall.…”

Section: Fig 3: Kkz Methodsmentioning

confidence: 99%

Review of Existing Methods for Finding Initial Clusters in K-means Algorithm

Singh¹,

Kaur²

2013

IJCA

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Clustering is one of the Data Mining tasks that can be used to cluster or group objects on the basis of their nearness to the central value. It has found many applications in the field of business, image processing, medical etc. K Means is one the method of clustering which is used widely because it is simple and efficient. The output of the K Means depends upon the chosen central values for clustering. So accuracy of the K Means algorithm depends much on the chosen central values. This paper presents the various methods evolved by researchers for finding initial clusters for K Means.

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“…However, the KKZ method sometimes finds bad clusters because unfortunately it depends on outlier data points [8]. This method has one obvious pitfall.…”

Section: Fig 3: Kkz Methodsmentioning

confidence: 99%

Review of Existing Methods for Finding Initial Clusters in K-means Algorithm

Singh¹,

Kaur²

2013

IJCA

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“…First part in the first iteration is initialization of kseeds for the k-means algorithm, and initialization of k-weights for each data point. We use k-means++ [44], [49] algorithm to obtain the initial seeds of the k-means clustering, while the kweights for each data point are initialized to zero. The second part is updating the k-weights [22].…”

Section: B Clustering Stepmentioning

confidence: 99%

Weighted Unsupervised Learning for 3D Object Detection

Kowsari¹,

Alassaf²

2016

ijacsa

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Abstract-This paper introduces a novel weighted unsupervised learning for object detection using an RGB-D camera. This technique is feasible for detecting the moving objects in the noisy environments that are captured by an RGB-D camera. The main contribution of this paper is a real-time algorithm for detecting each object using weighted clustering as a separate cluster. In a preprocessing step, the algorithm calculates the pose 3D position X, Y, Z and RGB color of each data point and then it calculates each data point's normal vector using the point's neighbor. After preprocessing, our algorithm calculates k-weights for each data point; each weight indicates membership. Resulting in clustered objects of the scene.

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“…Table 1 gives the data set descriptions. For each data set, the number of clusters (K) was set equal to the number of classes (K ′ ), as commonly seen in the related literature [53,7,100,94,2,21,64,86,24,25,41].…”

Section: Data Set Descriptionsmentioning

confidence: 99%

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Çelebi

Kingravi

2014

Partitional Clustering Algorithms

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Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erişoglu et al. performs surprisingly poorly.

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Careful Seeding Method based on Independent Components Analysis for k-means Clustering

Cited by 17 publications

References 18 publications

Review of Existing Methods for Finding Initial Clusters in K-means Algorithm

Review of Existing Methods for Finding Initial Clusters in K-means Algorithm

Weighted Unsupervised Learning for 3D Object Detection

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

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