Locally adaptive metrics for clustering high dimensional data

Domeniconi, Carlotta; Gunopulos, Dimitrios; Ma, Sheng; Yan, Bojun; Al-Razgan, Muna; Papadopoulos, Dimitris N.

doi:10.1007/s10618-006-0060-8

Cited by 215 publications

(175 citation statements)

References 25 publications

(31 reference statements)

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“…Besides mean shift and DBSCAN [7,10], we also performed comparisons against several other clustering algorithms: OPTICS [1,6], k-means [21], LAC [9], Aver-l (average-linkage clustering) [15], and EM (with a Gaussian mixture) [8]. OPTICS is a density based clustering algorithm which creates an augmented ordering of the data representing its clustering structure, and then retrieves DB-SCAN clusters as the final clustering result.…”

Section: Methodsmentioning

confidence: 99%

“…k-means, LAC, Aver-l, and EM require the number of clusters in input, which we set equal to the number of classes in the data. LAC requires an additional parameter (weight of the regularization term; see [9] for details), which we set to 0.2 throughout our experiments. Mean shift, DBSCAN, OPTICS, and Aver-l are deterministic for fixed parameter values.…”

Section: Methodsmentioning

confidence: 99%

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Boosted Mean Shift Clustering

Ren

Kamath

Domeniconi

et al. 2014

Machine Learning and Knowledge Discovery in Databases

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Abstract. Mean shift is a nonparametric clustering technique that does not require the number of clusters in input and can find clusters of arbitrary shapes. While appealing, the performance of the mean shift algorithm is sensitive to the selection of the bandwidth, and can fail to capture the correct clustering structure when multiple modes exist in one cluster. DBSCAN is an efficient density based clustering algorithm, but it is also sensitive to its parameters and typically merges overlapping clusters. In this paper we propose Boosted Mean Shift Clustering (BMSC) to address these issues. BMSC partitions the data across a grid and applies mean shift locally on the cells of the grid, each providing a number of intermediate modes (iModes). A mode-boosting technique is proposed to select points in denser regions iteratively, and DBSCAN is utilized to partition the obtained iModes iteratively. Our proposed BMSC can overcome the limitations of mean shift and DBSCAN, while preserving their desirable properties. Complexity analysis shows its potential to deal with large-scale data and extensive experimental results on both synthetic and real benchmark data demonstrate its effectiveness and robustness to parameter settings.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Boosted Mean Shift Clustering

Ren

Kamath

Domeniconi

et al. 2014

Machine Learning and Knowledge Discovery in Databases

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“…This TWvFKM weights both views and individual variables and is an extension to W-k-means. Domeniconi et al [15] have proposed the Locally Adaptive Clustering (LAC) algorithm which assigns a weight to each variable in each cluster.…”

Section: Fuzzy -Kmean Of Clustering Algorithmmentioning

confidence: 99%

Image Segmentation Using Two Weighted Variable Fuzzy K Means

Suganya¹,

Margaret²

2013

IJCATR

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Image segmentation is the first step in image analysis and pattern recognition. Image segmentation is the process of dividing an image into different regions such that each region is homogeneous. The accurate and effective algorithm for segmenting image is very useful in many fields, especially in medical image. This paper presents a new approach for image segmentation by applying k-means algorithm with two level variable weighting. In image segmentation, clustering algorithms are very popular as they are intuitive and are also easy to implement. The K-means and Fuzzy k-means clustering algorithm is one of the most widely used algorithms in the literature, and many authors successfully compare their new proposal with the results achieved by the k-Means and Fuzzy k-Means. This paper proposes a new clustering algorithm called TWfuzzy k-means, an automated two-level variable weighting clustering algorithm for segmenting object. In this algorithm, a variable weight is also assigned to each variable on the current partition of data. This could be applied on general images and/or specific images (i.e., medical and microscopic images). The proposed TWFuzzy k-means algorithm in terms of providing a better segmentation performance for various type of images. Based on the results obtained, the proposed algorithm gives better visual quality as compared to several other clustering methods.

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“…The computation stops when a quality criterion is satisfied or when a maximum number of iterations is achieved. Examples of kmeans like methods are: K-Harmonic Means [21], CURLER [20], LAC [6] and LWC/CLWC [4].…”

Section: Clusteringmentioning

confidence: 99%

QMAS: Querying, Mining and Summarization of Multi-modal Databases

Cordeiro

Guo

Haverkamp³

et al. 2010

2010 IEEE International Conference on Data Mining

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Given a large collection of images, very few of which have labels given a priori, how can we automatically assign the labels of the remaining majority, and make suggestion for images that may need brand new labels distinct from existing ones? Popular automatic labeling techniques usually scale super linearly with the size of the image set, and/or their performances degrade if limited images bear initial labels. In this paper, we propose QMAS, an efficient solution to the following problems: (i) low-labor labeling (L3) -given a collection of images, very few of which are already labeled with keywords, find the most suitable labels for the remaining ones; and (ii) mining and attention routing -with the same input set, output a number of top representative images and top outliers. We present experimental evaluation on three data sets of proprietary and public satellite images up to a size of 2.25GB. QMAS scales linearly with the number of images, obtaining better or equal accuracy while being up to 40 times faster than its baseline algorithm. With limited numbers of initial labels available, QMAS achieves a significant accuracy margin over the baseline approach. The application of QMAS to recommend representatives and spot outliers is also illustrated. The proposed framework could be generalized to solve similar content-based annotation and mining problems on other multi-modal databases.

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Locally adaptive metrics for clustering high dimensional data

Cited by 215 publications

References 25 publications

Boosted Mean Shift Clustering

Boosted Mean Shift Clustering

Image Segmentation Using Two Weighted Variable Fuzzy K Means

QMAS: Querying, Mining and Summarization of Multi-modal Databases

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