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The density peak based clustering algorithm is a recently proposed clustering approach. It uses the local density of each data and the distance to the nearest neighbor with higher density to isolate and identify the cluster centers. After the cluster centers are identified, the other data are assigned labels equaling to those of their nearest neighbors with higher density. This algorithm is simple...
The density peak based clustering algorithm is a simple yet effective clustering approach. This algorithm firstly calculates the local density of each data and the distance to the nearest neighbor with higher density. Based on the assumption that cluster centers are density peaks and they are relatively far from each other, this algorithm isolates the candidates of cluster centers from the non-center...
The particle size distribution (PSD) of a dispersed phase is a fundamental geometrical characteristic that needs to be determined from digital images for many industrial processes involving a multiphase flow. Nevertheless, when dealing with 2-D images, only the projections of the particles are visualized and therefore the particles can overlap each other. In this way, this paper aims to develop and...
The clustering algorithm by fast search and find of density peaks is shown to be a promising clustering approach. However, this algorithm involves manual selection of cluster centers, which is not convenient in practical applications. In this paper we discuss the correlation between density peaks and cluster centers. As a result, we present a new local density estimation method to highlight the uniqueness...
The recently proposed clustering algorithm based on density peaks is reported to generate very good clustering results. This algorithm is simple and efficient, and can be used to generate clusters of arbitrary shapes. However, the performance of this algorithm relies on the selection of the kernel in local density calculation. The original density peak based algorithm uses the cutoff kernel and Gaussian...
We introduce a novel bags-of-features framework based on relative position descriptors, modeling both spatial relations and shape information between the pairwise structural subparts of objects. First, we propose a hierarchical approach for the decomposition of complex objects into structural subparts, as well as their description using the concept of Force Histogram Decomposition (FHD). Then, an...
Superpixel decomposition methods are generally used as a pre-processing step to speed up image processing tasks. They group the pixels of an image into homogeneous regions while trying to respect existing contours. For all state-of-the-art superpixel decomposition methods, a trade-off is made between 1) computational time, 2) adherence to image contours and 3) regularity and compactness of the decomposition...
Support vector clustering (SVC) is a versatile clustering technique that is able to identify clusters of arbitrary shapes by exploiting the kernel trick. However, one hurdle that restricts the application of SVC lies in its sensitivity to the kernel parameter and the trade-off parameter. Although many extensions of SVC have been developed, to the best of our knowledge, there is still no algorithm...
The Levy Walk (or Levy flight) is a concept fromBiomathematics to describe the hunting–behaviour of manypredatory species. It is a very efficient way to find prey in avery short time frame. We now want to use this concept ina clustering–context to – if you so will – "hunt" for clusters. We describe how we convert this concept into an efficient wayto find cluster centres by linking the data...
In this paper, we present a dynamic clustering algorithm that efficiently deals with data streams and achieves several important properties which are not generally found together in the same algorithm. The dynamic clustering algorithm operates online in two different time-scale stages, a fast distance-based stage that generates micro-clusters and a density-based stage that groups the micro-clusters...
Clusters are well recognized regardless of their shape and of the dimensionality of the space in which they are embedded in traditional CFSFDP (Clustering by fast search and find of density peaks). But when large-scale dataset is processed, it takes too long time to calculate the distance between two data points. In this paper, we present a novel MapReduce-based CFSFDP clustering algorithm called...
Human body shapes are considered important information for fashion and clothing companies. In order to design better fitting clothes for the population or target customers, accurate analysis of body shapes is required. So far, most works on grouping body shapes are done using body measurements and classifying them into a given number of clusters. However, with the advancement in 3D body scanning technology,...
The class of density-based clustering algorithms excels in detecting clusters of arbitrary shape. DBSCAN, the most common representative, has been demonstrated to be useful in a lot of applications. Still the algorithm suffers from two drawbacks, namely a non-trivial parameter estimation for a given dataset and the limitation to data sets with constant cluster density. The first was already addressed...
One of the more challenging real-world problems in computational intelligence is to learn from non-stationary streaming data, also known as concept drift. Perhaps even a more challenging version of this scenario is when - following a small set of initial labeled data - the data stream consists of unlabeled data only. Such a scenario is typically referred to as learning in initially labeled nonstationary...
Fuzzy density-based clustering has been a challenge. Research has been focused on fuzzyfying the DBSCAN algorithm. Different methods have been proposed that use a fuzzy definition of core points within the DBSCAN algorithm. Our approach adapts the membership degree calculation known from fuzzy c-means by replacing the need for a distinguished centroid point by a more general cluster skeleton. These...
This paper extends our previous work on deriving meaningful storm patterns from very large rainfall data. In an earlier work, we described MapReduce-based algorithms to identify three types of the storms: local, hourly and overall storms. In general, local storms have temporal characteristics of the storms at a particular site, hourly storms have spatial characteristics of the storms at a particular...
In this paper, we present a new approach of distributed clustering for spatial datasets, based on an innovative and efficient aggregation technique. This distributed approach consists of two phases: 1) local clustering phase, where each node performs a clustering on its local data, 2) aggregation phase, where the local clusters are aggregated to produce global clusters. This approach is characterised...
Distance-based and density-based clustering algorithmsare often used on large spatial and arbitrary shape ofdata sets. However, some well-known clustering algorithms havetroubles when distribution of objects in the dataset varies, andthis may lead to a bad clustering result. Such bad performancesare more dramatically significant on high-dimensional dataset. Recently, Rodriguez and Laio proposed an...
Identifying the consumption patterns of electric customers and grouping them to classes according to their load characteristics can be very meaningful for power supply and demand side management in smart grid. Previously, tariff structures are mainly based on the type of activity. However, the type of activity and electrical behavior of the customer have poor relationship. Using clustering techniques...
Recovery of sparse signals with unknown clustering pattern in the case of having partial erroneous prior knowledge on the supports of the signal is considered. In this case, we provide a modified sparse Bayesian learning model to incorporate prior knowledge and simultaneously learn the unknown clustering pattern. For this purpose, we add one more layer to support-aided sparse Bayesian learning algorithm...
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