Recent Advances in Support Vector Clustering: Theory and Applications

Abstract

As an important boundary-based clustering algorithm, support vector clustering (SVC) can benefit many real applications owing to its capability of handling arbitrary cluster shapes, especially those directly or indirectly related to pattern exploration and description. As the application deepens, the importance of performance (i.e. criterions of accuracy and efficiency) of SVC increases. To identify gaps in the current methods and propose novel research directions for SVC, we present a survey of the literature in this area. Our approach is to classify the most recent advances into either theory or application. For theoretical contributions, advances related to parameter selection and optimization, dual-problem solutions, and cluster labeling are introduced. We also simultaneously summarize the advantages and drawbacks of each study. With respect to applications, we clearly describe eight groups of schemes based on SVC, either as individual or hybrid methods. Finally, we identify the gaps in SVC research and suggest several future research issues and trends.

Maximized Privacy-Preserving Outsourcing on Support Vector Clustering

“…Otherwise, it is up to O(dN 2 ) whose innermost operation calculates each kernel function's value of two d-dimensional samples. Although it seems to be time-consuming, it is much lower than O(N 3 ) required by the traditional methods which frequently need O(N 2 ) storage (see [4]). Further, the innermost operations for both situations are simple.…”

“…Clustering forms natural groupings of data samples that maximize intra-cluster similarity and minimize inter-cluster similarity. Inspired by support vector machines (SVMs), support vector clustering (SVC) has attracted many studies for remarkably handling clusters with arbitrary shape [1][2][3][4]. Various application areas are closely related to it, e.g., information retrieval and analysis, signal processing, traffic behavior identification, etc.…”

“…Various application areas are closely related to it, e.g., information retrieval and analysis, signal processing, traffic behavior identification, etc. [2,[4][5][6]. However, building a good clustering model requires a large number of valid training samples and a substantial iterative analysis under specific metrics; hence, it is frequently hard for individuals or small organizations to build their model on those quickly accumulated data.…”

“…However, building a good clustering model requires a large number of valid training samples and a substantial iterative analysis under specific metrics; hence, it is frequently hard for individuals or small organizations to build their model on those quickly accumulated data. The pricey storage of the kernel matrix and costly computations by the dual problem solver and a large number of sampling checks set big barriers to its applications [4,7,8]. To tackle this issue, a viable solution is to outsource its heavy workloads to the Cloud [9] with sufficient computing resources.…”

Parallel N-Path Quantification Hierarchical K-Means Clustering Algorithm for Video Retrieval

“…Extensive and good overviews of clustering algorithms can be found in the literature. 15,24,25,37 Most of the existing methods can work on moderately large datasets, where it is required to store the entire dataset in the main memory.…”