scholarly journals Inconsistency Measurement for Improving Logical Formula Clustering

2020 ◽  
Author(s):  
Yakoub Salhi
Keyword(s):  
Classical Logic ◽  
Logical Consequence ◽  
Main Idea ◽  
Heterogeneous Data ◽  
Formal Logic ◽  
Clustering Methods ◽  
Logical Formula

Formal logic can be used as a tool for representing complex and heterogeneous data such as beliefs, knowledge and preferences. This study proposes an approach for defining clustering methods that deal with bases of propositional formulas in classical logic, i.e., methods for dividing formula bases into meaningful groups. We first use a postulate-based approach for introducing an intuitive framework for formula clustering. Then, in order to characterize interesting clustering forms, we introduce additional properties that take into consideration different notions, such us logical consequence, overlapping, and consistent partition. Finally, we describe our approach that shows how the inconsistency measures can be involved in improving the task of formula clustering. The main idea consists in using the measures for quantifying the quality of the inconsistent clusters. In this context, we propose further properties that allow characterizing interesting aspects related to the amount of inconsistency.

ALGEBRA OF PREDICATES AND RELATED GEOMETRIC MODELS CREATION IN REGARDS OF UNIFIED STATE EXAM IN INFORMATICS (RUSSIAN EGE)

2019 ◽  
pp. 40-47
Author(s):  
E. A. Mironchik
Keyword(s):  
Geometric Model ◽  
Main Idea ◽  
Formal Logic ◽  
High Complexity ◽  
Geometric Models ◽  
Complexity Problem ◽  
Logical Expression ◽  
State Examination

The article discusses the method of solving the task 18 on the Unified State Examination in Informatics (Russian EGE). The main idea of the method is to write the conditions of the problem utilizing the language of formal logic, using elementary predicates. According to the laws of logic the resulting complex logical expression would be transformed into an expression, according to which a geometric model is supposed to be constructed which allows to obtain an answer. The described algorithm does allow high complexity problem to be converted into a simple one.

scholarly journals Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gregoire Preud’homme ◽  
Kevin Duarte ◽  
Kevin Dalleau ◽  
Claire Lacomblez ◽  
Emmanuel Bresso ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Latent Class ◽  
Latent Class Model ◽  
Real Life ◽  
Heterogeneous Data ◽  
Mixed Data ◽  
Categorical Variables ◽  
Clustering Methods ◽  
Model Based ◽  
Partitioning Around Medoids

AbstractThe choice of the most appropriate unsupervised machine-learning method for “heterogeneous” or “mixed” data, i.e. with both continuous and categorical variables, can be challenging. Our aim was to examine the performance of various clustering strategies for mixed data using both simulated and real-life data. We conducted a benchmark analysis of “ready-to-use” tools in R comparing 4 model-based (Kamila algorithm, Latent Class Analysis, Latent Class Model [LCM] and Clustering by Mixture Modeling) and 5 distance/dissimilarity-based (Gower distance or Unsupervised Extra Trees dissimilarity followed by hierarchical clustering or Partitioning Around Medoids, K-prototypes) clustering methods. Clustering performances were assessed by Adjusted Rand Index (ARI) on 1000 generated virtual populations consisting of mixed variables using 7 scenarios with varying population sizes, number of clusters, number of continuous and categorical variables, proportions of relevant (non-noisy) variables and degree of variable relevance (low, mild, high). Clustering methods were then applied on the EPHESUS randomized clinical trial data (a heart failure trial evaluating the effect of eplerenone) allowing to illustrate the differences between different clustering techniques. The simulations revealed the dominance of K-prototypes, Kamila and LCM models over all other methods. Overall, methods using dissimilarity matrices in classical algorithms such as Partitioning Around Medoids and Hierarchical Clustering had a lower ARI compared to model-based methods in all scenarios. When applying clustering methods to a real-life clinical dataset, LCM showed promising results with regard to differences in (1) clinical profiles across clusters, (2) prognostic performance (highest C-index) and (3) identification of patient subgroups with substantial treatment benefit. The present findings suggest key differences in clustering performance between the tested algorithms (limited to tools readily available in R). In most of the tested scenarios, model-based methods (in particular the Kamila and LCM packages) and K-prototypes typically performed best in the setting of heterogeneous data.

scholarly journals Edge Computing for Data Anomaly Detection of Multi-Sensors in Underground Mining

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 302
Author(s):  
Chunde Liu ◽  
Xianli Su ◽  
Chuanwen Li
Keyword(s):  
Energy Consumption ◽  
Anomaly Detection ◽  
Underground Mining ◽  
Heterogeneous Data ◽  
Edge Computing ◽  
Sensor Nodes ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Clustering Methods ◽  
Safety Warning

There is a growing interest in safety warning of underground mining due to the huge threat being faced by those working in underground mining. Data acquisition of sensors based on Internet of Things (IoT) is currently the main method, but the data anomaly detection and analysis of multi-sensors is a challenging task: firstly, the data that are collected by different sensors of underground mining are heterogeneous; secondly, real-time is required for the data anomaly detection of safety warning. Currently, there are many anomaly detection methods, such as traditional clustering methods K-means and C-means. Meanwhile, Artificial Intelligence (AI) is widely used in data analysis and prediction. However, K-means and C-means cannot directly process heterogeneous data, and AI algorithms require equipment with high computing and storage capabilities. IoT equipment of underground mining cannot perform complex calculation due to the limitation of energy consumption. Therefore, many existing methods cannot be directly used for IoT applications in underground mining. In this paper, a multi-sensors data anomaly detection method based on edge computing is proposed. Firstly, an edge computing model is designed, and according to the computing capabilities of different types of devices, anomaly detection tasks are migrated to different edge devices, which solve the problem of insufficient computing capabilities of the devices. Secondly, according to the requirements of different anomaly detection tasks, edge anomaly detection algorithms for sensor nodes and sink nodes are designed respectively. Lastly, an experimental platform is built for performance comparison analysis, and the experimental results show that the proposed algorithm has better performance in anomaly detection accuracy, delay, and energy consumption.

Qualitŕ della vita e invecchiamento della popolazione. Materiali per un'ipotesi alternativa alla questione pensionistica

2009 ◽  
pp. 67-91
Author(s):  
Adele Bianco
Keyword(s):  
Quality Of Life ◽  
Life Quality ◽  
Main Idea ◽  
Living Condition ◽  
Climate And Environmental Change ◽  
Safety And Health ◽  
Positive Ageing ◽  
The Impact ◽  
Main Factor

The topic of this article is quality of life and ageing process specially focused on today young generations and their coming retirement situation. The main idea is that quality of life is increasing, that means longer, safer and better living condition; consequently positive ageing processes mean also reforming retirement sector. The hypothesis carried out in this paper is an alternative one. Despite of the positive trends, we describe how three main factor of nowadays life are, on the contrary, turning into worse condition the future of young generations and their coming life situation. Firstly we consider the socio-economic aspects, the impact on health and the implications connected with precarious work. Secondly we consider pollution and its effects on health, life quality and life expectation. Thirdly we pay attention about climate and environmental change and their effect on health, life quality and expectation. In conclusion, the retirement future of today young generations may be very different (worse) than expected. The paper in based on WHO, IPCC and European Agency for Safety and Health at Work data and reports.Key words: Young generations; Coming quality of life; Population ageing and future of retirement question.Parole chiave: Giovani generazioni; Qualitŕ della vita; Invecchiamento della popolazione e pensioni.

scholarly journals X-Mark: a benchmark for node-attributed community discovery algorithms

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Salvatore Citraro ◽  
Giulio Rossetti
Keyword(s):  
State Of The Art ◽  
Graph Clustering ◽  
Clustering Methods ◽  
Community Discovery ◽  
Attributed Graphs ◽  
Node Labels ◽  
Discovery Algorithms ◽  
Shed Light

AbstractGrouping well-connected nodes that also result in label-homogeneous clusters is a task often known as attribute-aware community discovery. While approaching node-enriched graph clustering methods, rigorous tools need to be developed for evaluating the quality of the resulting partitions. In this work, we present X-Mark, a model that generates synthetic node-attributed graphs with planted communities. Its novelty consists in forming communities and node labels contextually while handling categorical or continuous attributive information. Moreover, we propose a comparison between attribute-aware algorithms, testing them against our benchmark. Accordingly to different classification schema from recent state-of-the-art surveys, our results suggest that X-Mark can shed light on the differences between several families of algorithms.

scholarly journals Model-driven geospatial web service composition

2014 ◽  
Vol XL-6 ◽  
pp. 7-11
Author(s):  
W. Du ◽  
H. Fan ◽  
J. Li ◽  
H. Wang
Keyword(s):  
Web Service ◽  
Service Composition ◽  
Process Model ◽  
Heterogeneous Data ◽  
Web Service Composition ◽  
Dynamic Binding ◽  
Service Chain ◽  
Model Driven ◽  
Geospatial Web Service

A lot of work has been done on the geospatial service composition to support advanced processing, spatial calculation, and invoking of heterogeneous data. However, the quality of service chain is rarely considered and the process model cannot be reused. A modeldriven way of geospatial web service composition is proposed in this work, the service composition is treated as an optimization problem by <i>GwcsFlow</i> model and dynamic binding mechanism. The case of facility location analysis is provided to demonstrate the improvements in geospatial service composition through optimization algorithms.

scholarly journals Adaptive Unified Differential Evolution for Clustering

2018 ◽  
Vol 12 (1) ◽  
pp. 53
Author(s):  
Maulida Ayu Fitriani ◽  
Aina Musdholifah ◽  
Sri Hartati
Keyword(s):  
Differential Evolution ◽  
Fitness Function ◽  
Single Mutation ◽  
Clustering Methods ◽  
Mutation Strategy ◽  
Clustering Quality ◽  
Silhouette Index ◽  
Mutation Strategies ◽  
Time Required

Various clustering methods to obtain optimal information continues to evolve one of its development is Evolutionary Algorithm (EA). Adaptive Unified Differential Evolution (AuDE), is the development of Differential Evolution (DE) which is one of the EA techniques. AuDE has self adaptive scale factor control parameters (F) and crossover-rate (Cr).. It also has a single mutation strategy that represents the most commonly used standard mutation strategies from previous studies.The AuDE clustering method was tested using 4 datasets. Silhouette Index and CS Measure is a fitness function used as a measure of the quality of clustering results. The quality of the AuDE clustering results is then compared against the quality of clustering results using the DE method.The results show that the AuDE mutation strategy can expand the cluster central search produced by ED so that better clustering quality can be obtained. The comparison of the quality of AuDE and DE using Silhoutte Index is 1:0.816, whereas the use of CS Measure shows a comparison of 0.565:1. The execution time required AuDE shows better but Number significant results, aimed at the comparison of Silhoutte Index usage of 0.99:1 , Whereas on the use of CS Measure obtained the comparison of 0.184:1.

Investigating cluster validation metrics for optimal number of clusters determination

2021 ◽  
pp. 1-16
Author(s):  
Aikaterini Karanikola ◽  
Charalampos M. Liapis ◽  
Sotiris Kotsiantis
Keyword(s):  
Real World ◽  
Optimal Number ◽  
Cluster Validation ◽  
Clustering Methods ◽  
Number Of Clusters ◽  
Validity Indices ◽  
Selection Of ◽  
Specific Distance ◽  
Optimal Number Of Clusters

In short, clustering is the process of partitioning a given set of objects into groups containing highly related instances. This relation is determined by a specific distance metric with which the intra-cluster similarity is estimated. Finding an optimal number of such partitions is usually the key step in the entire process, yet a rather difficult one. Selecting an unsuitable number of clusters might lead to incorrect conclusions and, consequently, to wrong decisions: the term “optimal” is quite ambiguous. Furthermore, various inherent characteristics of the datasets, such as clusters that overlap or clusters containing subclusters, will most often increase the level of difficulty of the task. Thus, the methods used to detect similarities and the parameter selection of the partition algorithm have a major impact on the quality of the groups and the identification of their optimal number. Given that each dataset constitutes a rather distinct case, validity indices are indicators introduced to address the problem of selecting such an optimal number of clusters. In this work, an extensive set of well-known validity indices, based on the approach of the so-called relative criteria, are examined comparatively. A total of 26 cluster validation measures were investigated in two distinct case studies: one in real-world and one in artificially generated data. To ensure a certain degree of difficulty, both real-world and generated data were selected to exhibit variations and inhomogeneity. Each of the indices is being deployed under the schemes of 9 different clustering methods, which incorporate 5 different distance metrics. All results are presented in various explanatory forms.

SPATIAL ASPECTS OF THE REGION’S SUSTAINABLE DEVELOPMENT

2021 ◽  
Vol 1 (64) ◽  
pp. 81-88
Author(s):  
Tamara V. Uskova ◽  
Keyword(s):  
Sustainable Development ◽  
Rural Areas ◽  
Main Idea ◽  
State Regulation ◽  
Spatial Factor ◽  
National Settlement ◽  
National Settlement System ◽  
High Level ◽  
The Town

One of the most relevant issues causing concern of the world community is the ensurance of sustainable development. The problem was raised in the second half of the 20th century and has not been solved up to the present yet. The main idea of the article is to consider the spatial factor of sustainable development. The author shows that there is a high level of territories’ socio-economic differentiation in the Russian Federation. The problem of increasing polarization both between the center and periphery, and between the town and countryside has become particularly urgent. Rural areas are significantly lagging behind in terms of the population’s level and quality of life, and the rates of socio-economic development. The trends of rural extinction are intensifying and, as a result, there emerge the sparsity of economic space, and the decrease in stability of the national settlement system and development of countries as a whole. The increase of socio-economic problems in rural areas requires strengthening of the state regulation concerning the territories’ spatial development

The Playout Control Management

2011 ◽  
pp. 689-702
Author(s):  
Mirko Luca Lobina ◽  
Luigi Atzori ◽  
Fabrizio Boi
Keyword(s):  
Quality Of Service ◽  
Service Provider ◽  
Service Providers ◽  
State Of The Art ◽  
Main Idea ◽  
The State ◽  
Ip Telephony ◽  
Open Standard ◽  
Control Management

IP Telephony provides a way for an enterprise to extend consistent communication services to all employees, whether they are in main campus locations, at branch offices, or working remotely, also with a mobile phone. IP Telephony transmits voice communications over a network using open standard-based Internet protocols. This is both the strength and weakness of IP Telephony as the involved basic transport protocols (RTP, UDP, and IP) are not able to natively guarantee the required application quality of service (QoS). From the point of view of an IP Telephony Service Provider this definitely means possible waste of clients and money. Specifically the problem is at two different levels: i) in some countries, wherelong distance and particularly international call tariffs are high, perhaps due to a lack of competition or due to cross subsidies to other services, the major opportunity for IP Telephony Service Providers is for price arbitrage. This means working on diffusion of an acceptable service, although not at high quality levels; ii) in other countries, where different IP Telephony Service Providers already exist, the problem is competition for offering the best possible quality. The main idea behind this chapter is to analyze specifically the state of the art playout control strategies with the following aims: i) propose the reader the technical state of the art playout control management and planning strategies (overview of basic KPIs for IP Telephony); ii) compare the strategies IP Telephony Service Provider can choose with the aim of saving money and offering a better quality of service; iii) introduce also the state of the art quality index for IP Telephony, that is a set of algorithms for taking into account as many factors as possible to evaluate the service quality; iv) provide the reader with examples on some economic scenarios of IP Telephony.

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