Добірка наукової літератури з теми "Maximum Common Subsequences"

The paper proposes a high-performance method of radiosignal classification based on spectral-covariance evaluation of signals. At the same time, multi-stage processing of overlapping subsequences of readings of the investigated radiosignal in the time and frequency domain is carried out. At the first stage, it is proposed to evaluate the parameters of the radiosignal based on the found power spectral density of the investigated signal. To determine the spectral density of the radiosignal, subsequences of readings obtained from the array of input readings of the investigated signal are formed. The maximum shift between two adjacent subsequences is chosen, that is, the initial realization of the signal is presented in the form of overlapping subsequences. Taking into account that two adjacent subsequences have part of common processed readings, the paper proposes an expression that for each new subsequence of input readings would take into account the coefficients of the discrete Fourier transform, which were determined for the previous subsequence of the input signal. During the following stages, the shape of the spectrum of the analyzed radiosignal is compared with the spectrum samples specified by the operator of the radiocontrol system. Comparison of the spectrum of the investigated radiosignal and the spectral mask is based on the determined correlation coefficient. The approximation of the value of the correlation coefficient to unity characterizes the degree of linear relationship between the spectrum of the signal and the mask. This makes it possible to determine the type and positions of radiochannels based on the obtained sequence of correlation coefficient values for different shifts of the spectral mask. The analysis of the effectiveness of the proposed method confirmed that thanks to the developed method, it is possible to increase the productivity of the spectral-covariance evaluation of radio signals by 2.0Г·8.9 times, depending on the volume of the analyzed implementation of the radiosignal and the number of overlapping subsequences. The maximum performance factor is achieved when the initial implementation of the radiosignal is divided into 64 overlapping subsequences. The proposed method can be used in automated radiotechnical control systems to monitor the radiosituation in real time.

<p class="Abstrak"><em>Coronavirus</em> merupakan salah satu parasit yang menyerang sistem pernapasan manusia. Peningkatan kasus <em>coronavirus</em> berlangsung sangat cepat dan menyebar ke berbagai negara. Oleh karena itu, World Health Organization (WHO) menetapkan <em>Coronavirus</em> sebagai pandemi. Hal ini mengakibatkan seluruh kegiatan yang sebelumnya tatap muka atau luar jaringan (luring) menjadi dalam jaringan (daring), termasuk kegiatan belajar mengajar. Dengan ditetapkannya pembelajaran secara daring menyebabkan adanya opini yang bersifat pro dan kontra dari berbagai kalangan masyarakat. Opini tersebut akan digunakan dalam penelitian ini dan akan diolah terlebih dahulu dalam tahap <em>preprocessing</em>. Metode yang digunakan dalam penelitian ini adalah <em>Longest Common Subsequences</em> (LCS) dan <em>Support Vector Machine</em> (SVM) dengan data sebesar 500 yang terbagi menjadi 250 data berlabel positif dan 250 data berlabel negatif. Dari 500 data tersebut dibagi menjadi 450 data untuk data latih dan 50 data untuk data uji. Dengan menggunakan metode <em>Longest Common Subsequences</em> untuk perbaikan kata dan metode <em>Support Vector Machine</em> untuk klasifikasi dengan nilai parameter terbaik yaitu <em>learning rate</em> (γ) = 0,0001, <em>lambda</em> (λ) = 0,1, <em>complexity</em> (C) = 0,001, <em>epsilon</em> (ϵ) = 0,0001 dan iterasi maksimum = 50 dapat menghasilkan nilai rata-rata hasil evaluasi yaitu <em>precision</em> = 0,5653, <em>recall</em> = 0,948, <em>f-measure</em> = 0,7047 dan <em>accuracy</em> = 0,598. Hasil pengujian tersebut mununjukkan bahwa dengan menambahkan metode <em>Longest Common Subsequences</em> untuk perbaikan kata dapat meningkatkan tingkat akurasi yang sebelumnya hanya 0,59 menjadi 0,598.</p><p class="Abstrak"> </p><p class="Abstrak"><strong>Abstract</strong></p><p class="Abstrak"> <em>Coronavirus is a parasite that attacks the human respiratory system. The increase incases coronavirus took place very fast and spread to various countries. Therefore, the World Health Organization (WHO) has designated Coronavirus as a pandemic. This results in all activities that were previously face-to-face or offline (offline) becoming online (online), including teaching and learning activities. With the establishment of online learning, there are pro and contra opinions from various circles of society. This opinion will be used in this research and will be processed first in the stage preprocessing. The method used in this research is Longest Common Subsequences (LCS) and Support Vector Machine (SVM) with 500 data divided into 250 data labeled positive and 250 data labeled negative. Of the 500 data is divided into 450 data for training data and 50 data for test data. By using the method Longest Common Subsequences for word improvement and the method Support Vector Machine for classification with the best parameter values, namely learning rate (γ) = 0.0001, lambda (λ) = 0.1, complexity (C) = 0.001, epsilon (ϵ ) = 0.0001 and the maximum iteration = 50 can produce the average value of the evaluation results, namely precision = 0.5653, recall = 0.948, f-measure = 0.7047 and accuracy = 0.598. The test results show that by adding method of Longest Common Subsequences for word improvement, it can increase the level of accuracy which was previously only 0.59 to 0.598.</em></p>

The maintenance and updating of Statistics Austria's business register requires a regularly matching of the register against other data sources; one of them is the register of tax units of the Austrian Federal Ministry of Finance. The matching process is based on string comparison via bigrams of enterprise names and addresses, and a quality class approach assigning pairs of register units into classes of different compliance (i.e., matching quality) based on bigram similarity values and the comparison of other matching variables, like the NACE code or the year of foundation.Based on methodological research concerning matching techniques carried out in the DIECOFIS project, an empirical comparison of the bigram method and other string matching techniques was conducted: the edit distance, the Jaro algorithm and the Jaro-Winkler algorithm, the longest common subsequence and the maximal match were selected as appropriate alternatives and evaluated in the study.This paper briey introduces Statistics Austria's business register and the corresponding maintenance process and reports on the results of the empirical study.

Une séquence est une suite d’éléments ordonnés comme par exemple les trajectoires de déplacement ou les séquences d’achats de produits dans un supermarché. La fouille de séquences est un domaine de la fouille de données qui vise à extraire des motifs séquentiels fréquents à partir d’un ensemble de séquences, où ces motifs sont le plus souvent des sous-séquences. Plusieurs algorithmes ont été proposés pour l’extraction des motifs séquentiels fréquents. Avec l’évolution des capacités de calcul, la tâche d’extraction des motifs séquentiels fréquents est devenue plus rapide. La difficulté réside alors dans le trop grand nombre de motifs séquentiels extraits, qui en rend difficile la lisibilité et donc l’interprétation. On parle de déluge de motifs. L’Analyse Formelle de Concepts (AFC) est un domaine d’analyse de données permettant d’identifier des relations à partir d’un ensemble de données binaires. Les structures de motifs étendent l’AFC pour traiter des données complexes comme les séquences. La plateforme GALACTIC implémente l’algorithme Next Priority Concept qui propose une approche d’extraction de motifs pour des données hétérogènes et complexes. Il permet un calcul de motifs génériques à travers des descriptions spécifiques d’objets par des prédicats monadiques. Il propose également de raffiner un ensemble d’objets à travers des stratégies d’explorations spécifiques, ce qui permet de réduire le nombre de motifs. Dans ce travail, nous nous intéressons à l’analyse de données séquentielles en utilisant GALACTIC. Nous proposons plusieurs descriptions et stratégies adaptées aux séquences. Nous proposons également des mesures de qualité non supervisées pour pouvoir comparer entre les motifs obtenus. Une analyse qualitative et quantitative est menée sur des jeux de données réels et synthétiques afin de montrer l’efficacité de notre approche
A sequence is a sequence of ordered elements such as travel trajectories or sequences of product purchases in a supermarket. Sequence mining is a domain of data mining that aims an extracting frequent sequential patterns from a set of sequences, where these patterns are most often common subsequences. Support is a monotonic measure that defines the proportion of data sharing a sequential pattern. Several algorithms have been proposed for frequent sequential pattern extraction. With the evolution of computing capabilities, the task of frequent sequential pattern extraction has become faster. The difficulty then lies in the large number of extracted sequential patterns, which makes it difficult to read and therefore to interpret. We speak about "deluge of patterns". Formal Concept Analysis (FCA) is a field of data analysis for identifying relationships in a set of binary data. Pattern structures extend FCA to handle complex data such as sequences. The GALACTIC platform implements the Next Priority Concept algorithm which proposes a pattern extraction approach for heterogeneous and complex data. It allows a generic pattern computation through specific descriptions of objects by monadic predicates. It also proposes to refine a set of objects through specific exploration strategies, which allows to reduce the number of patterns. In this work, we are interested in the analysis of sequential data using GALACTIC. We propose several descriptions and strategies adapted to sequences. We also propose unsupervised quality measures to be able to compare between the obtained patterns. A qualitative and quantitative analysis is conducted on real and synthetic datasets to show the efficiency of our approach