Text Analysis, Data Mining, and Visualizations in Literary Scholarship

2013 ◽  
Author(s):  
Tanya Clement
Keyword(s):  
Data Mining ◽  
Text Analysis ◽  
Analysis Data ◽  
Literary Scholarship

scholarly journals Anomaly Detection in Arabic Texts using Ngrams and Self Organizing Maps

2021 ◽  
Vol 11 (04) ◽  
pp. 17-27
Author(s):  
Abdulwahed Almarimi ◽  
Asmaa Salem
Keyword(s):  
Data Mining ◽  
Anomaly Detection ◽  
Text Analysis ◽  
Analysis Data ◽  
Point Of View ◽  
Statistical Characteristics ◽  
Self Organizing Maps ◽  
Written Text ◽  
N Gram ◽  
Self Organizing

Every written text in any language has one author or more authors (authors have their individual sublanguage). An analysis of text if authors are not known could be done using methods of data analysis, data mining, and structural analysis. In this paper, two methods are described for anomaly detections: ngrams method and a system of Self-Organizing Maps working on sequences built from a text. there are analyzed and compared results of usable methods for discrepancies detection based on character n-gram profiles (the set of character n-gram normalized frequencies of a text) for Arabic texts. Arabic texts were analyzed from many statistical characteristics point of view. We applied some heuristics for measurements of text parts dissimilarities. We evaluate some Arabic texts and show its parts they contain discrepancies and they need some following analysis for anomaly detection. The analysis depends on selected parameters prepared in xperiments. The system is trained to input sequences after which it determines text parts with anomalies using a cumulative error and winner analysis in the networks. Both methods have been tested on Arabic texts and they have a perspective contribution to text analysis.

Advanced Statistical Tools for Improving Yield and Reliability

1999 ◽  
Author(s):  
Richard C. Kittler
Keyword(s):  
Data Mining ◽  
Analysis Data ◽  
Neural Nets ◽  
Future Trends ◽  
Statistical Tools ◽  
Mining Technology ◽  
Explanatory Variables ◽  
Data Coverage ◽  
Statistical Approaches ◽  
Adequate Data

Abstract Analysis of manufacturing data as a tool for failure analysts often meets with roadblocks due to the complex non-linear behaviors of the relationships between failure rates and explanatory variables drawn from process history. The current work describes how the use of a comprehensive engineering database and data mining technology over-comes some of these difficulties and enables new classes of problems to be solved. The characteristics of the database design necessary for adequate data coverage and unit traceability are discussed. Data mining technology is explained and contrasted with traditional statistical approaches as well as those of expert systems, neural nets, and signature analysis. Data mining is applied to a number of common problem scenarios. Finally, future trends in data mining technology relevant to failure analysis are discussed.

Report on SEBD 2020

2020 ◽  
Vol 54 (2) ◽  
pp. 1-5
Author(s):  
Maristella Agosti ◽  
Maurizio Atzori ◽  
Paolo Ciaccia ◽  
Letizia Tanca
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Text Analysis ◽  
Database Systems ◽  
Multimedia Content ◽  
Security Issues ◽  
Multimedia Content Analysis ◽  
Knowledge Graphs ◽  
Virtual Conference ◽  
And Diffusion

This paper reports on the 28th Italian Symposium on Advanced Database Systems (SEBD 2020), held online as a virtual conference from the 21st to the 24th of June 2020. The topics that were addressed in this edition of the conference were organized in the sessions: ontologies and data integration, anomaly detection and dependencies, text analysis and search, deep learning, noSQL data, trajectories and diffusion, health and medicine, context and ranking, social and knowledge graphs, multimedia content analysis, security issues, and data mining.

Application of Data Mining in Smart Grid Technology

2021 ◽  
pp. 815-827
Author(s):  
Lipi Chhaya ◽  
Paawan Sharma ◽  
Adesh Kumar ◽  
Govind Bhagwatikar
Keyword(s):  
Data Mining ◽  
Smart Grid ◽  
Power Grid ◽  
Analysis Data ◽  
Consumer Participation ◽  
Distribution Automation ◽  
Grid Technology ◽  
Self Healing ◽  
Grid Operation ◽  
Smart Grid Technology

Smart grid technology is a radical approach for improvisation in existing power grid. Some of the significant features of smart grid technology are bidirectional communication, AMI, SCADA, renewable integration, active consumer participation, distribution automation, and complete management of entire grid through wireless communication standards and technologies. Management of complex, hierarchical, and heterogeneous smart grid infrastructure requires data collection, storage, processing, analysis, retrieval, and communication for self-healing and complete automation. Data mining techniques can be an effective solution for smart grid operation and management. Data mining is a computational process for data analysis. Data scrutiny is unavoidable for unambiguous knowledge discovery as well as decision making practices. Data mining is inevitable for analysis of various statistics associated with power generation, distribution automation, data communications, billing, consumer participation, and fault diagnosis in smart power grid.

Process Optimization of Candy Production Based on Data Mining

2011 ◽  
Vol 282-283 ◽  
pp. 662-665 ◽  
Author(s):  
Jia Liang Zhang ◽  
Jian Guo Yang ◽  
Shou Guo Shen ◽  
Han Yan Chen
Keyword(s):  
Data Mining ◽  
Production Process ◽  
Process Parameters ◽  
Production Efficiency ◽  
Analysis Data ◽  
Mining Method ◽  
Process Data ◽  
Increase Production Efficiency ◽  
Display Module

There are complicated correlations between process parameters and quality indicators in candy manufacturing. The objective of this work is to develop an optimization system of candy production process to improve final candy quality and to increase production efficiency. The study is conducted by using an artificial neural network data mining method to obtain optimization knowledge of process parameters from large amount of saved process data. The software platform including data processing, statistic analysis, data mining and graphical display module was developed and the quality forecasting models for typical processing operations were discussed. Experiments indicated that the system can optimize and predict the quality of candy production process effectively.

scholarly journals Data Mining based Prediction of Demand in Indian Market for Refurbished Electronics

2020 ◽  
Vol 2 (3) ◽  
pp. 153-159
Author(s):  
Dr. V. Suma
Keyword(s):  
Data Mining ◽  
Real World ◽  
Research Work ◽  
Analysis Data ◽  
Business Environment ◽  
Customer Behavior ◽  
The Real ◽  
Market Factors ◽  
Real World Datasets ◽  
The Impact

There has been an increasing demand in the e-commerce market for refurbished products across India during the last decade. Despite these demands, there has been very little research done in this domain. The real-world business environment, market factors and varying customer behavior of the online market are often ignored in the conventional statistical models evaluated by existing research work. In this paper, we do an extensive analysis of the Indian e-commerce market using data-mining approach for prediction of demand of refurbished electronics. The impact of the real-world factors on the demand and the variables are also analyzed. Real-world datasets from three random e-commerce websites are considered for analysis. Data accumulation, processing and validation is carried out by means of efficient algorithms. Based on the results of this analysis, it is evident that highly accurate prediction can be made with the proposed approach despite the impacts of varying customer behavior and market factors. The results of analysis are represented graphically and can be used for further analysis of the market and launch of new products.

scholarly journals Analysis of Characteristics of Tennis Singles Matches Based on 5G and Data Mining Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ming Li ◽  
Qinsheng Li ◽  
Yuening Li ◽  
Yunkun Cui ◽  
Xiufeng Zhao ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Statistical Methods ◽  
Analysis Data ◽  
Scientific Basis ◽  
Experimental Simulation ◽  
Mining Technology ◽  
Offensive Tactics ◽  
Analysis System ◽  
Scientific Decision

The level of technical and tactical decision-making in a tennis game has a very important impact on the outcome of the game. How to discover the characteristics and rules of the game from a large amount of technical and tactical data, how to overcome the shortcomings of traditional statistical methods, and how to provide a scientific basis for correct decision-making are a top priority. Based on 5G and association analysis data mining theory, we established a data mining model for tennis technical offensive tactics and association rules and conducted specific case studies. It can calculate the maximization and distribution rate of certain technologies, also distinguish between the athlete’s gain and loss rate and the spatial position on the track, and use artificial statistical methods to cause errors and subjective participation. This solution provides objective and scientific decision support for this problem and is used in the decision-making of the landing point in tennis match technology and tactics. Experimental simulation shows that the data mining technology analysis system used for regional tennis matches is more concise, efficient, and accurate than traditional movie analysis methods.

scholarly journals Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data

10.2196/13209 ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. e13209 ◽  
Author(s):  
Afsaneh Doryab ◽  
Daniella K Villalba ◽  
Prerna Chikersal ◽  
Janine M Dutcher ◽  
Michael Tumminia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Statistical Analysis ◽  
Social Isolation ◽  
Analysis Data ◽  
Behavioral Phenotypes ◽  
Passive Sensing
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