Land use image classification through optimum-path forest clustering

Land use classification has been paramount in the last years, since we can identify illegal land use and also to monitor deforesting areas. Although one can find several research works in the literature that address this problem, we propose here the land use recognition by means of Optimum-Path Forest Clustering (OPF), which has never been applied to this context up to date. Experiments among Optimum-Path Forest, Mean Shift and K-Means demonstrated the robustness of OPF for automatic land use classification of images obtained by CBERS-2B and Ikonos-2 satellites. © 2011 IEEE.

Spatial clustering applied to health area

The significant volume of work accidents in the cities causes an expressive loss to society. The development of Spatial Data Mining technologies presents a new perspective for the extraction of knowledge from the correlation between conventional and spatial attributes. One of the most important techniques of the Spatial Data Mining is the Spatial Clustering, which clusters similar spatial objects to find a distribution of patterns, taking into account the geographical position of the objects. Applying this technique to the health area, will provide information that can contribute towards the planning of more adequate strategies for the prevention of work accidents. The original contribution of this work is to present an application of tools developed for Spatial Clustering which supply a set of graphic resources that have helped to discover knowledge and support for management in the work accidents area. © 2011 IEEE.

Post-processing association rules with clustering and objective measures

The post-processing of association rules is a difficult task, since a large number of patterns can be obtained. Many approaches have been developed to overcome this problem, as objective measures and clustering, which are respectively used to: (i) highlight the potentially interesting knowledge in domain; (ii) structure the domain, organizing the rules in groups that contain, somehow, similar knowledge. However, objective measures don't reduce nor organize the collection of rules, making the understanding of the domain difficult. On the other hand, clustering doesn't reduce the exploration space nor direct the user to find interesting knowledge, making the search for relevant knowledge not so easy. This work proposes the PAR-COM (Post-processing Association Rules with Clustering and Objective Measures) methodology that, combining clustering and objective measures, reduces the association rule exploration space directing the user to what is potentially interesting. Thereby, PAR-COM minimizes the user's effort during the post-processing process.

Characterization of the temperature, load and damage effects using piezoelectric transducer patches based on fuzzy clustering

Structural Health Monitoring (SHM) denotes a system with the ability to detect and interpret adverse changes in a structure. One of the critical challenges for practical implementation of SHM system is the ability to detect damage under changing environmental conditions. This paper aims to characterize the temperature, load and damage effects in the sensor measurements obtained with piezoelectric transducer (PZT) patches. Data sets are collected on thin aluminum specimens under different environmental conditions and artificially induced damage states. The fuzzy clustering algorithm is used to organize the sensor measurements into a set of clusters, which can attribute the variation in sensor data due to temperature, load or any induced damage.

Electrical consumers data clustering through optimum-path forest

Non-technical losses identification has been paramount in the last decade. Since we have datasets with hundreds of legal and illegal profiles, one may have a method to group data into subprofiles in order to minimize the search for consumers that cause great frauds. In this context, a electric power company may be interested in to go deeper a specific profile of illegal consumer. In this paper, we introduce the Optimum-Path Forest (OPF) clustering technique to this task, and we evaluate the behavior of a dataset provided by a brazilian electric power company with different values of an OPF parameter. © 2011 IEEE.

QK-Means: A clustering technique based on community detection and K-Means for deployment of cluster head nodes

Wireless Sensor Networks (WSN) are a special kind of ad-hoc networks that is usually deployed in a monitoring field in order to detect some physical phenomenon. Due to the low dependability of individual nodes, small radio coverage and large areas to be monitored, the organization of nodes in small clusters is generally used. Moreover, a large number of WSN nodes is usually deployed in the monitoring area to increase WSN dependability. Therefore, the best cluster head positioning is a desirable characteristic in a WSN. In this paper, we propose a hybrid clustering algorithm based on community detection in complex networks and traditional K-means clustering technique: the QK-Means algorithm. Simulation results show that QK-Means detect communities and sub-communities thus lost message rate is decreased and WSN coverage is increased. © 2012 IEEE.

Labeling methods for association rule clustering

Although association mining has been highlighted in the last years, the huge number of rules that are generated hamper its use. To overcome this problem, many post-processing approaches were suggested, such as clustering, which organizes the rules in groups that contain, somehow, similar knowledge. Nevertheless, clustering can aid the user only if good descriptors be associated with each group. This is a relevant issue, since the labels will provide to the user a view of the topics to be explored, helping to guide its search. This is interesting, for example, when the user doesn't have, a priori, an idea where to start. Thus, the analysis of different labeling methods for association rule clustering is important. Considering the exposed arguments, this paper analyzes some labeling methods through two measures that are proposed. One of them, Precision, measures how much the methods can find labels that represent as accurately as possible the rules contained in its group and Repetition Frequency determines how the labels are distributed along the clusters. As a result, it was possible to identify the methods and the domain organizations with the best performances that can be applied in clusters of association rules.

Boosting optimum-path forest clustering through harmony Search and its applications for intrusion detection in computer networks

In this paper we propose a nature-inspired approach that can boost the Optimum-Path Forest (OPF) clustering algorithm by optimizing its parameters in a discrete lattice. The experiments in two public datasets have shown that the proposed algorithm can achieve similar parameters' values compared to the exhaustive search. Although, the proposed technique is faster than the traditional one, being interesting for intrusion detection in large scale traffic networks. © 2012 IEEE.

Automatic visual dictionary generation through Optimum-Path Forest clustering

Image categorization by means of bag of visual words has received increasing attention by the image processing and vision communities in the last years. In these approaches, each image is represented by invariant points of interest which are mapped to a Hilbert Space representing a visual dictionary which aims at comprising the most discriminative features in a set of images. Notwithstanding, the main problem of such approaches is to find a compact and representative dictionary. Finding such representative dictionary automatically with no user intervention is an even more difficult task. In this paper, we propose a method to automatically find such dictionary by employing a recent developed graph-based clustering algorithm called Optimum-Path Forest, which does not make any assumption about the visual dictionary's size and is more efficient and effective than the state-of-the-art techniques used for dictionary generation. © 2012 IEEE.

Intrusion detection in computer networks using optimum-path forest clustering

Nowadays, organizations face the problem of keeping their information protected, available and trustworthy. In this context, machine learning techniques have also been extensively applied to this task. Since manual labeling is very expensive, several works attempt to handle intrusion detection with traditional clustering algorithms. In this paper, we introduce a new pattern recognition technique called Optimum-Path Forest (OPF) clustering to this task. Experiments on three public datasets have showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, since it outperformed some state-of-the-art unsupervised techniques. © 2012 IEEE.

New label free CA125 detection based on gold nanostructured screen-printed electrode

This paper describes the optimisation and the analytical performances of a label-free impedimetric immunosensor for the detection of tumour marker CA125 based on gold nanoparticles modified screen-printed graphite electrode. Experimental conditions of each step for the developed immunosensor were studied and optimised. The immunosensor response varied linearly (r2 = 0.996) with antigen concentration between 0 and 100 U/mL. The estimated detection limit was 6.7 U/mL. The electrochemical immunosensor allowed unambiguous identification of CA125, while no significant non-specific signal was detected in the case of all negative controls. The analytical usefulness of the impedimetric immunosensor was finally demonstrated analysing serum samples. © 2012 Elsevier B.V. All rights reserved.

An optimised electrochemical biosensor for the label-free detection of C-reactive protein in blood

C-reactive protein (CRP) is an acute phase protein whose levels are increased in many disorders. There exists, in particular, a great deal of interest in the correlation between blood serum levels and the severity of risk for cardiovascular disease. A sensitive, label-free, non-amplified and reusable electrochemical impedimetric biosensor for the detection of CRP in blood serum was developed herein based on controlled and coverage optimised antibody immobilization on standard polycrystalline gold electrodes. Charge transfer resistance changes were highly target specific, linear with log. CRP. concentration across a 0.5-50. nM range and associated with a limit of detection of 176. pM. Significantly, the detection limits are better than those of current CRP clinical methods and the assays are potentially cheap, relatively automated, reusable, multiplexed and highly portable. The generated interfaces were capable not only of comfortably quantifying CRP across a clinically relevant range of concentrations but also of doing this in whole blood serum with interfaces that were, subsequently, reusable. The importance of optimising receptor layer resistance in maximising assay sensitivity is also detailed. © 2012.

The role of hierarchical morphologies in the superior gas sensing performance of CuO-based chemiresistors

The development of gas sensors with innovative designs and advanced functional materials has attracted considerable scientific interest given their potential for addressing important technological challenges. This work presents new insight towards the development of high-performance p-type semiconductor gas sensors. Gas sensor test devices, based on copper (II) oxide (CuO) with innovative and unique designs (urchin-like, fiber-like, and nanorods), are prepared by a microwave-assisted synthesis method. The crystalline composition, surface area, porosity, and morphological characteristics are studied by X-ray powder diffraction, nitrogen adsorption isotherms, field-emission scanning electron microscopy and high-resolution transmission electron microscopy. Gas sensor measurements, performed simultaneously on multiple samples, show that morphology can have a substantial influence on gas sensor performance. An assembly of urchin-like structures is found to be most effective for hydrogen detection in the range of parts-per-million at 200 °C with 300-fold larger response than the previously best reported values for semiconducting CuO hydrogen gas sensors. These results show that morphology plays an important role in the gas sensing performance of CuO and can be effectively applied in the further development of gas sensors based on p-type semiconductors. High-performance gas sensors based on CuO hierarchical morphologies with in situ gas sensor comparison are reported. Urchin-like morphologies with high hydrogen sensitivity and selectivity that show chemical and thermal stability and low temperature operation are analyzed. The role of morphological influences in p-type gas sensor materials is discussed. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Metrics to support the evaluation of association rule clustering

Many topics related to association mining have received attention in the research community, especially the ones focused on the discovery of interesting knowledge. A promising approach, related to this topic, is the application of clustering in the pre-processing step to aid the user to find the relevant associative patterns of the domain. In this paper, we propose nine metrics to support the evaluation of this kind of approach. The metrics are important since they provide criteria to: (a) analyze the methodologies, (b) identify their positive and negative aspects, (c) carry out comparisons among them and, therefore, (d) help the users to select the most suitable solution for their problems. Some experiments were done in order to present how the metrics can be used and their usefulness. © 2013 Springer-Verlag GmbH.

Label free redox capacitive biosensing

A surface confined redox group contributes to an interfacial charging (quantifiable by redox capacitance) that can be sensitively probed by impedance derived capacitance spectroscopy. In generating mixed molecular films comprising such redox groups, together with specific recognition elements (here antibodies), this charging signal is able to sensitively transduce the recognition and binding of specific analytes. This novel transduction method, exemplified here with C-reactive protein, an important biomarker of cardiac status and general trauma, is equally applicable to any suitably prepared interfacial combination of redox reporter and receptor. The assays are label free, ultrasensitive, highly specific and accompanied by a good linear range. © 2013 Elsevier B.V.