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Fuzzy clustering techniques, especially Fuzzy C-Means clustering method (FCM), is a popular algorithm widely used in the images segmentation. However, as the conventional FCM doesn't optimize data in feature space and doesn't involve any spatial information, it is sensitive to the noise. In the paper, we presented a novel FCM clustering algorithm based on kernel spatial information to segment the...
This research report about Fuzzy C-Means for Deforestation Identification Based On Remote Sensing Image. Deforestation means that changes forest area into another functions. Clustering is a method of classify objects into related groups (clusters). While, Fuzzy C-Means clustering is a technique that each data is determined by the degree of membership. In this research, the data used are MODIS EVI...
Forest fires are a serious problem that occurs repeatedly in Indonesia. Fire events can be predicted by monitoring the datasets of hotspots which are recorded through remote sensing satellite. This study aims to build a web application that performs clustering on the hotspots data. This application implements DBSCAN algorithm using Shiny web framework for R programming language. Clustering is performed...
Recent studies have suggested significant differences in motor performances of Parkinson's Disease (PD) patients who have L-dopa induced dyskinesias (LIDs), even when off of L-dopa medication. The pathophysiology of LIDs remains obscure, so applying data-mining techniques to the patients' motor performance may provide some heuristic insight. This paper investigated visually-guided tracking performance...
We present a novel method for re-creating the static structure of cluttered office environments - which we define as the “meta-room” - from multiple observations collected by an autonomous robot equipped with an RGB-D depth camera over extended periods of time. Our method works directly with point clusters by identifying what has changed from one observation to the next, removing the dynamic elements...
This paper presents an embarrassingly parallel hopping window algorithm to remove noise from Lidar (Light detection and ranging) sensor for robot mapping applications. The algorithm works by analyzing the density of Lidar data inside a window which hops over the entire input sensor data. For faster execution of the algorithm, multiple window hopping is done intelligently without omitting the processing...
Clustering algorithms have been widely used in many different applications such as pattern recognition, data mining. It is unsupervised learning algorithm. At the same, the data sets of similarity partition belong to the same group; otherwise data sets divide other groups in the clustering algorithms. The interval fuzzy c-means (IFCM) clustering method was proposed to deal with symbolic interval data...
A video surveillance system is primarily designed to track key objects, or people exhibiting suspicious behavior, as they move from one position to another and record it for possible future use. The critical parts of an object tracking algorithm are object segmentation, image clusters detection, and identification and tracking of these image clusters. The major roadblocks of the tracking algorithm...
Document Image Binarization refers to the task of transforming a scanned image of a handwritten or printed document into a bi-level representation containing only characters and background. Here, we address the historic document image binarization problem using a three-stage methodology. Firstly, we remove possible stains and noise from the document image by estimating the document background image...
In masquerade attack, attacker impersonates legitimate user. Most of the masquerade detection techniques done so far are based on supervised learning techniques. But here in this paper masquerade detection based on unsupervised learning techniques are used. Various clustering algorithms used are K-Means, K-Medoid, Agglomerative clustering algorithm and DBSCAN. A comparative study is done based on...
Fuzzy clustering has been extensively used in brain magnetic resonance (MR) image segmentation. However, due to the existence of noise and intensity inhomogeneity, many segmentation algorithms suffer from limited accuracy. In this paper, we propose a fuzzy clustering algorithm via enhanced spatially constraint for brain MR image segmentation. A novel spatial factor is proposed by incorporating the...
Clustering algorithms based on Grid are attractive for the task of data partition in spatial database. In the background of big data more and more research focuses on how to solve the conflict between efficiency and accuracy of clustering. Existing Grid-based clustering algorithms generally have a high time efficiency without considering the distribution of the data inside a grid. In this paper, a...
An important task in maritime search and inspection involves re-acquiring and identifying underwater objects by surveying the objects from multiple angles. Because of false contacts related to clutter on the sea floor, the objects are often detected in dramatically different densities in a given area. Previously developed methods to plan survey paths on groups of contacts led to efficient paths when...
In this paper we propose a noise detection system based on similarities between instances. Having a data set with instances that belongs to multiple classes, a noise instance denotes a wrongly classified record. The similarity between different labeled instances is determined computing distances between them using several metrics among the standard ones. In order to ensure that this approach is computational...
The use of word senses in place of surface word forms has been shown to improve performance on many computational tasks, including intelligent web search. In this paper we propose a novel approach to automatic discovery of word senses from raw text, a task referred to as Word Sense Induction (WSI). Almost all the WSI approaches proposed in the literature dealt with monolingual data and only very few...
Digital imaging is widely used in applications such as medical, biometrics, multimedia,…etc. In many cases, images are transmitted through Internet from one point to another. During image acquisition and transmission, factors such as moving objects, sensor quality, and channel interferences may result in additive noise. The presence of noise affects image quality. Image denoising process attempts...
DBSCAN is a clustering algorithm based on density. It can divide regions which have a high density for clusters, shield the noise effectively and discover clusters of arbitrary shape and any size from dataset. However, DBSCAN algorithm needs to traverse dataset to find core objects, so it results in large amount of I/O cost when processing large-scale datasets. A fast algorithm (BEDBSCAN) is developed...
A robust hyper spectral unmixing algorithm that finds multiple sets of end members is introduced. The algorithm, called Robust Context Dependent Spectral Unmixing (RCDSU), combines the advantages of context dependent unmixing and robust clustering. RCDSU adapts the unmixing to different regions, or contexts, of the spectral space. It combines fuzzy and possibilistic clustering and linear unmixing...
This paper presents an extended Isomap algorithm called SL-Isomap (SOINN Landmark Isomap). We adopt SOINN (Self-Organizing Incremental Neural Network) algorithm to choose the reasonable number of landmarks automatically. SOINN landmarks are able to represent topological structure of unsupervised data in the high dimensional input space. Then L-Isomap (Landmark Isomap) algorithm is used to find low...
Nowadays we communicate in a digital universe. In fact the amount of data (structured and unstructured) is exploding. That's what we call Big Data. The voluminous data are in the most of cases noisy and overlapping, their clustering makes critical challenges. In addition validating resulting partitions is a serious problem. In this paper we present a new fuzzy validity index able to interpret the...
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