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With the rapid advances in digital technology, the multimedia documents have been growing ubiquitously. The analysis of this huge repository of multimedia documents requires efficient organization of documents. Multimedia document clustering organizes the multimedia documents with common multimedia topics. The important step of multimedia document clustering is computing the similarity between multimedia...
This paper addresses a specific example of nonperiodic translation symmetry and presents an algorithm to automatically detect multiple poles, or their shadows, in aerial imagery by looking for consistent and overlapping regions of self-similarity across a non-urban scene. The algorithm does not rely on having a pole template or knowing its exact size. For each image patch, similar regions (or blobs)...
Crowdsourced, or human computation based clustering algorithms usually rely on relative distance comparisons, as these are easier to elicit from human workers than absolute distance information. We build upon existing work on correlation clustering, a well-known non-parametric approach to clustering, and present a novel clustering algorithm for human computation. We first define a novel variant of...
Monitoring energy consumption and diagnosing abnormal behavior will enable utilities to introduce strategies to improve system resiliency, stability, and to meet energy efficiency targets. The deployment of advanced metering infrastructure (AMI) enables utilities to collect various raw data from its customers and networks. This paper presents contextual anomaly detection algorithm to detect irregular...
In the training of the radial basis function network (RBFN), feature selection and classifier design are two tasks commonly addressed in separated processes. The former is related to the number of input nodes, whereas the latter is associated with the design of the hidden layer. Hence, this paper presents an algorithm to train a RBFN based on differential evolution (DE), which simultaneously adjusts...
A cluster formation algorithm is proposed to save the wastage of energy in cooperative spectrum sensing (CSS), in which small number of groups called clusters are made using fuzzy c-means (FCM). Based on spatial correlation, only limited number of SUs are selected from each cluster, whose sensing information is forwarded to their cluster head (CHs). The primary goal of cognitive radio network is spectrum...
Matrix factorization is a popular low dimensional representation approach that plays an important role in many pattern recognition and computer vision domains. Among them, convex and semi-nonnegative matrix factorizations have attracted considerable interest, owing to its clustering interpretation. On the other hand, the generalized correlation function (correntropy) as the error measure does not...
Bike sharing systems (BSSs) have become common in many cities worldwide, providing a new transportation mode for residents' commutes. However, the management of these systems gives rise to many problems. As the bike pick-up demands at different places are unbalanced at times, the systems have to be rebalanced frequently. Rebalancing the bike availability effectively, however, is very challenging as...
Clustering is a popular method to deal with the problem for mode identification of multimode processes. Unlike traditional distance-based clustering methods, in this paper, a new correlation-based bi-partition hierarchical clustering (CBHC) method is proposed, which classifies the observations according to their correlation relationships rather than their distances. Motivated by an existing correlation-based...
In this paper, we propose novel user clustering schemes for downlink non-orthogonal multiple access (NOMA) system, where an N-antenna base station (BS) selects and serves 2N users from K single antenna user equipments (UEs) (K ≥ 2N and K is even). In particular, we propose a signal difference and alignment (SDA) framework to achieve fair and spectral efficient user clustering for NOMA with flexible...
A low-complexity algorithm is presented that clusters sensor nodes based on similarity in the sensed signals. This feature makes it an enabler for distributed detection of events that are impossible to identify using information available to a single node. The algorithm does not require system training prior to deployment nor does it assume statistical knowledge of the signal. Experimental results...
Correlation clustering is a NP-hard problem, and for large graphs finding even just a good approximation of the optimal solution is a hard task. In previous articles we have suggested a contraction method and its divide and conquer variant. In this article we present several improvements of this method (preprocessing, quasi-parallelism, etc.) and prepare it for parallelism. Based on speed tests we...
Lower limb bones or lower limb component related to the torso with pelvic ankle interference can be fractured. Fractures can be detected automatically take advantage x-ray images performed using feature extraction methods. Feature Extraction helpful to know existence and location of fracture with x-ray images. This research apply Gray Level Co-Occurrence Matrix (GLCM) and K-Means Clustering Algorithm...
Document clustering groups documents of certain similar characteristics in one cluster. Document clustering has shown advantages on organization, retrieval, navigation and summarization of a huge amount of text documents on Internet. This paper presents a novel, unsupervised approach for clustering single-author documents into groups based on authorship. The key novelty is that we propose to extract...
The analysis of biological data is a challenging problem in bioinformatics and data mining field. Given the complexity of the analysis of biological information, several methods have been proposed for analyzing this biological information in databases mostly in the form of genetic sequences and protein structures. Actually, genetic sequences are represented by matrices that indicate the expression...
Clustering techniques have gained great popularity in neuroscience data analysis especially in analysing data from complex experiment paradigm where it is hard to apply traditional model-based method. However, when employing clustering analysis, many clustering algorithms are available nowadays and even with an individual clustering algorithm, choices like parameter settings and distance metrics are...
This paper presents a study of how speech features have comparable parameters amongst blood relations. Mel Frequency Cepstral Coefficients (MFCC) has been used for extracting the features of input speech signal, along with vector quantization through modified k-means LBG (Linde, Buzo, and Gray) algorithm are implemented to analyse and estimate the similarity to perform related studies. The study is...
This paper proposes an improvement to the PageRank algorithm. Most existing PageRank algorithms expect a strong correlation among consecutively accessed webpages, which in reality should be a fuzzy relationship when a user accesses pages on an arbitrarily basis. We mine data from search-behavior logs by analyzing chronological sequential patterns, and cluster all webpages using fuzzy C clustering...
Existing clustering algorithms need to specify the number of clusters and to select initial points using human input, which lead to inferior clustering and optimisation outputs. Here, an improved grey decision-making model based on the thought of affinity propagation algorithm and grey correlation analysis is proposed to solve these problems. According to the panel data class and the inter-class candidate...
Nowadays the activity recognition based on multiple wearable sensors is still a challenging task due to the diversity of human activities. The application of unsupervised classification is helpful to discovery new activity classes and improve the activity classification model. Therefore, a new multi-sensor activity recognition scheme using the two-dimensional principal component analysis (2DPCA) and...
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