Search results for "Cluster Analysis"
showing 10 items of 848 documents
Multi-functional Protein Clustering in PPI Networks
2008
Protein-Protein Interaction (PPI) networks contain valuable information for the isolation of groups of proteins that participate in the same biological function. Many proteins play different roles in the cell by taking part in several processes, but isolating the different processes in which a protein is involved is often a difficult task. In this paper we present a method based on a greedy local search technique to detect functional modules in PPI graphs. The approach is conceived as a generalization of the algorithm PINCoC to generate overlapping clusters of the interaction graph in input. Due to this peculiarity, multi-facets proteins are allowed to belong to different groups correspondi…
Randomized Hough Transform for Ellipse Detection with Result Clustering
2005
Our research is focused on the development of robust machine vision algorithms for pattern recognition. We want to provide robotic systems the ability to understand more on the external real world. In this paper, we describe a method for detecting ellipses in real world images using the randomized Hough transform with result clustering. A preprocessing phase is used in which real world images are transformed - noise reduction, greyscale transform, edge detection and final binarization - in order to be processed by the actual ellipse detector. The ellipse detector filters out false ellipses that may interfere with the final results. Due to the fact that usually more "virtual" ellipses are de…
Automatic Detection of Infantile Hemangioma using Convolutional Neural Network Approach
2020
Infantile hemangioma is the most common tumor of childhood. This study proposes an automatic detection as a preliminary step for a further accurate monitoring tool to evaluate the clinical status of hemangioma. For the detection of hemangioma pixels, a convolutional neural network (CNN) was trained on patches of two classes (hemangioma and nonhemangioma) from the train dataset, and then it was used to classify all the pixels of the region of interest from the test dataset. In order to evaluate the results of segmentation obtained with CNN, the region of interest of the test dataset was also segmented using two classical methods of segmentation: fuzzy c-means clustering (FCM) and segmentatio…
Probabilistic Transition-Based Approach for Detecting Application-Layer DDoS Attacks in Encrypted Software-Defined Networks
2017
With the emergence of cloud computing, many attacks, including Distributed Denial-of-Service (DDoS) attacks, have changed their direction towards cloud environment. In particular, DDoS attacks have changed in scale, methods, and targets and become more complex by using advantages provided by cloud computing. Modern cloud computing environments can benefit from moving towards Software-Defined Networking (SDN) technology, which allows network engineers and administrators to respond quickly to the changing business requirements. In this paper, we propose an approach for detecting application-layer DDoS attacks in cloud environment with SDN. The algorithm is applied to statistics extracted from…
Semisupervised kernel orthonormalized partial least squares
2012
This paper presents a semisupervised kernel orthonormalized partial least squares (SS-KOPLS) algorithm for non-linear feature extraction. The proposed method finds projections that minimize the least squares regression error in Hilbert spaces and incorporates the wealth of unlabeled information to deal with small size labeled datasets. The method relies on combining a standard RBF kernel using labeled information, and a generative kernel learned by clustering all available data. The positive definiteness of the kernels is proven, and the structure and information content of the derived kernels is studied. The effectiveness of the proposed method is successfully illustrated in standard UCI d…
Applying logistic regression to relevance feedback in image retrieval systems
2007
This paper deals with the problem of image retrieval from large image databases. A particularly interesting problem is the retrieval of all images which are similar to one in the user's mind, taking into account his/her feedback which is expressed as positive or negative preferences for the images that the system progressively shows during the search. Here we present a novel algorithm for the incorporation of user preferences in an image retrieval system based exclusively on the visual content of the image, which is stored as a vector of low-level features. The algorithm considers the probability of an image belonging to the set of those sought by the user, and models the logit of this prob…
Unsupervised clustering method for pattern recognition in IIF images
2017
Autoimmune diseases are a family of more than 80 chronic, and often disabling, illnesses that develop when underlying defects in the immune system lead the body to attack its own organs, tissues, and cells. Diagnosis of autoimmune pathologies is based on research and identification of antinuclear antibodies (ANA) through indirect immunofluorescence (IIF) method and is performed by analyzing patterns and fluorescence intensity. We propose here a method to automatically classify the centromere pattern based on the grouping of centromeres on the cells through a clustering K-means algorithm. The described method was tested on a public database (MIVIA). The results of the test showed an Accuracy…
An algorithm for earthquakes clustering based on maximum likelihood
2007
In this paper we propose a clustering technique set up to separate and find out the two main components of seismicity: the background seismicity and the triggered one. We suppose that a seismic catalogue is the realization of a non homogeneous space-time Poisson clustered process, with a different parametrization for the intensity function of the Poisson-type component and of the clustered (triggered) component. The method here proposed assigns each earthquake to the cluster of earthquakes, or to the set of independent events, according to the increment to the likelihood function, computed using the conditional intensity function estimated by maximum likelihood methods and iteratively chang…
Restricted Neighborhood Search Clustering Revisited: An Evolutionary Computation Perspective
2013
Protein-protein interaction networks have been broadly studied in the last few years, in order to understand the behavior of proteins inside the cell. Proteins interacting with each other often share common biological functions or they participate in the same biological process. Thus, discovering protein complexes made of groups of proteins strictly related, can be useful to predict protein functions. Clustering techniques have been widely employed to detect significative biological complexes. In this paper, we integrate one of the most popular network clustering techniques, namely the Restricted Neighborhood Search Clustering (RNSC), with evolutionary computation. The two cost functions in…
Automatic detection of cervical cells in Pap-smear images using polar transform and k-means segmentation
2016
We introduce a novel method of cell detection and segmentation based on a polar transformation. The method assumes that the seed point of each candidate is placed inside the nucleus. The polar representation, built around the seed, is segmented using k-means clustering into one candidate-nucleus cluster, one candidate-cytoplasm cluster and up to three miscellaneous clusters, representing background or surrounding objects that are not part of the candidate cell. For assessing the natural number of clusters, the silhouette method is used. In the segmented polar representation, a number of parameters can be conveniently observed and evaluated as fuzzy memberships to the non-cell class, out of …