Mining in Music Databases

2011 ◽  
pp. 340-374
Author(s):  
Ioannis Karydis ◽  
Alexandros Nanopoulos ◽  
Yannis Manolopoulos
Keyword(s):  
Data Mining ◽  
Statistical Models ◽  
Complex Structure ◽  
Digital Audio ◽  
Practical Applications ◽  
Acoustic Data ◽  
Music Classification ◽  
Musical Scores ◽  
Music Databases ◽  
Music Data Mining

This chapter provides a broad survey of music data mining, including clustering, classification and pattern discovery in music. The data studied is mainly symbolic encodings of musical scores, although digital audio (acoustic data) is also addressed. Throughout the chapter, practical applications of music data mining are presented. Music data mining addresses the discovery of knowledge from music corpora. This chapter encapsulates the theory and methods required in order to discover knowledge in the form of patterns for music analysis and retrieval, or statistical models for music classification and generation. Music data, with their temporal, highly structured and polyphonic character, introduce new challenges for data mining. Additionally, due to their complex structure and their subjectivity to inaccuracies caused by perceptual effects, music data present challenges in knowledge representation as well.

Mining in Music Databases

2009 ◽  
pp. 35-59
Author(s):  
Ioannis Karydis ◽  
Alexandros Nanopoulos ◽  
Yannis Manolopoulos
Keyword(s):  
Data Mining ◽  
Statistical Models ◽  
Complex Structure ◽  
Digital Audio ◽  
Practical Applications ◽  
Acoustic Data ◽  
Music Classification ◽  
Musical Scores ◽  
Music Databases ◽  
Music Data Mining

This chapter provides a broad survey of music data mining, including clustering, classification and pattern discovery in music. The data studied is mainly symbolic encodings of musical scores, although digital audio (acoustic data) is also addressed. Throughout the chapter, practical applications of music data mining are presented. Music data mining addresses the discovery of knowledge from music corpora. This chapter encapsulates the theory and methods required in order to discover knowledge in the form of patterns for music analysis and retrieval, or statistical models for music classification and generation. Music data, with their temporal, highly structured and polyphonic character, introduce new challenges for data mining. Additionally, due to their complex structure and their subjectivity to inaccuracies caused by perceptual effects, music data present challenges in knowledge representation as well.

Mining in Music Databases

2008 ◽  
pp. 3586-3610
Author(s):  
Ioannis Karydis ◽  
Alexandros Nanopoulos ◽  
Yannis Manolopoulos
Keyword(s):  
Data Mining ◽  
Statistical Models ◽  
Complex Structure ◽  
Digital Audio ◽  
Practical Applications ◽  
Acoustic Data ◽  
Music Classification ◽  
Musical Scores ◽  
Music Databases ◽  
Music Data Mining

This chapter provides a broad survey of music data mining, including clustering, classification and pattern discovery in music. The data studied is mainly symbolic encodings of musical scores, although digital audio (acoustic data) is also addressed. Throughout the chapter, practical applications of music data mining are presented. Music data mining addresses the discovery of knowledge from music corpora. This chapter encapsulates the theory and methods required in order to discover knowledge in the form of patterns for music analysis and retrieval, or statistical models for music classification and generation. Music data, with their temporal, highly structured and polyphonic character, introduce new challenges for data mining. Additionally, due to their complex structure and their subjectivity to inaccuracies caused by perceptual effects, music data present challenges in knowledge representation as well.

Big Data Visualisation and Visual Analytics for Music Data Mining

2018 ◽  
Author(s):  
Katrina E. Barkwell ◽  
Alfredo Cuzzocrea ◽  
Carson K. Leung ◽  
Ashley A. Ocran ◽  
Jennifer M. Sanderson ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Visual Analytics ◽  
Data Visualisation ◽  
Music Data Mining

A structured model for evaluating countermeasures at highway–railway grade crossings

2005 ◽  
Vol 32 (4) ◽  
pp. 627-635 ◽  
Author(s):  
Young-Jin Park ◽  
Frank F Saccomanno
Keyword(s):  
Data Mining ◽  
Statistical Models ◽  
Decision Support Tool ◽  
Cost Effective ◽  
Expected Number ◽  
Structured Model ◽  
Support Tool ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Grade Crossings

Various countermeasures can be introduced to reduce collisions at highway–railway grade crossings. These countermeasures may take different forms, such as passive and (or) active driver warning devices, supplementary traffic controls (four quadrant barriers, wayside horn, closed circuit television (CCTV) monitoring, etc.), illumination, signage and highway speed limit, etc. In this research, we present a structured model that makes use of data mining techniques to estimate the effect of changes in countermeasures on the expected number of collisions at a given crossing. This model serves as a decision-support tool for the evaluation and development of cost-effective and practicable safety program at highway–railway grade crossings. The use of data mining techniques helps to resolve many of the problems associated with conventional statistical models used to predict the expected number of collisions for a given type of crossing. Statistical models introduce biases that limit their ability to fully represent the relationship between selected countermeasures and resultant collisions for a mix of crossing attributes. This paper makes use of Canadian inventory and collision data to illustrate the potential merits of the proposed model to provide decision support.Key words: highway–railway grade crossing, collision prediction model, countermeasures, Poisson regression.

Data mining for building knowledge bases: techniques, architectures and applications

2016 ◽  
Vol 31 (2) ◽  
pp. 97-123 ◽  
Author(s):  
Alfred Krzywicki ◽  
Wayne Wobcke ◽  
Michael Bain ◽  
John Calvo Martinez ◽  
Paul Compton
Keyword(s):  
Data Mining ◽  
Knowledge Base ◽  
Question Answering ◽  
Knowledge Bases ◽  
Event Extraction ◽  
Data Sources ◽  
Small Scale ◽  
Knowledge Mining ◽  
Practical Applications ◽  
Unstructured Text

AbstractData mining techniques for extracting knowledge from text have been applied extensively to applications including question answering, document summarisation, event extraction and trend monitoring. However, current methods have mainly been tested on small-scale customised data sets for specific purposes. The availability of large volumes of data and high-velocity data streams (such as social media feeds) motivates the need to automatically extract knowledge from such data sources and to generalise existing approaches to more practical applications. Recently, several architectures have been proposed for what we callknowledge mining: integrating data mining for knowledge extraction from unstructured text (possibly making use of a knowledge base), and at the same time, consistently incorporating this new information into the knowledge base. After describing a number of existing knowledge mining systems, we review the state-of-the-art literature on both current text mining methods (emphasising stream mining) and techniques for the construction and maintenance of knowledge bases. In particular, we focus on mining entities and relations from unstructured text data sources, entity disambiguation, entity linking and question answering. We conclude by highlighting general trends in knowledge mining research and identifying problems that require further research to enable more extensive use of knowledge bases.

The Role of Data Mining for Business Intelligence in Knowledge Management

2015 ◽  
pp. 12-33 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Human Resources ◽  
Organizational Performance ◽  
Business Intelligence ◽  
Statistical Significance ◽  
Practical Applications ◽  
Business Goals ◽  
Emerging Trends

This chapter introduces the role of Data Mining (DM) for Business Intelligence (BI) in Knowledge Management (KM), thus explaining the concept of KM, BI, and DM; the relationships among KM, BI, and DM; the practical applications of KM, BI, and DM; and the emerging trends toward practical results in KM, BI, and DM. In order to solve existing BI problems, this chapter also describes practical applications of KM, BI, and DM (in the fields of marketing, business, manufacturing, and human resources) and the emerging trends in KM, BI, and DM (in terms of larger databases, high dimensionality, over-fitting, evaluation of statistical significance, change of data and knowledge, missing data, relationships among DM fields, understandability of patterns, integration of other DM systems, and users' knowledge and interaction). Applying DM for BI in the KM environments will enhance organizational performance and achieve business goals in the digital age.

Online Clustering and Outlier Detection

Data Mining ◽  
2013 ◽  
pp. 142-158
Author(s):  
Baoying Wang ◽  
Aijuan Dong
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
Future Research ◽  
Online Data ◽  
Data Set ◽  
Online Clustering ◽  
Practical Applications ◽  
Online Fraud ◽  
Mining Areas ◽  
Two Phases

Clustering and outlier detection are important data mining areas. Online clustering and outlier detection generally work with continuous data streams generated at a rapid rate and have many practical applications, such as network instruction detection and online fraud detection. This chapter first reviews related background of online clustering and outlier detection. Then, an incremental clustering and outlier detection method for market-basket data is proposed and presented in details. This proposed method consists of two phases: weighted affinity measure clustering (WC clustering) and outlier detection. Specifically, given a data set, the WC clustering phase analyzes the data set and groups data items into clusters. Then, outlier detection phase examines each newly arrived transaction against the item clusters formed in WC clustering phase, and determines whether the new transaction is an outlier. Periodically, the newly collected transactions are analyzed using WC clustering to produce an updated set of clusters, against which transactions arrived afterwards are examined. The process is carried out continuously and incrementally. Finally, the future research trends on online data mining are explored at the end of the chapter.

scholarly journals Continuous Directional Water Delivery on the 3D-Printed Arrowhead Microstructure Array

Materials ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1043 ◽  
Author(s):  
Lihua Liang ◽  
Wei Wang ◽  
Junjun Chen ◽  
Kunpeng Jiang ◽  
Yufeng Sheng ◽  
...  
Keyword(s):  
Complex Structure ◽  
External Energy ◽  
Liquid Transport ◽  
Practical Applications ◽  
Transport Distance ◽  
Transport Length ◽  
Simulation And Experiment ◽  
3D Printed ◽  
The Relationship ◽  
Microstructure Array

Unidirectional transport is attracting increasing attention in the field of microfluidics, because it does not require an external energy supply. However, most of the current self-driving structures are still plagued with persistent problems that restrict their practical applications. These include low transport velocity, short transport distance, and complex structure. This work reports the design of a new arrowhead microstructure array, on which liquid transport can reach speeds of 23 mm/s and the ratio of transport length to channel width (L/R) can reach up to approximately 40. This structure drives liquid through a unique arrow conformation, which can induce capillary force and arrest the reverse motion of the liquid simultaneously. By means of theory, simulation, and experiment, we have studied the mechanism of liquid transport on this structure. We provide a detailed discussion of the relationship between the velocity of liquid transport and the microstructural dimensions. The findings may inspire the design of novel, unidirectional, liquid-spreading surfaces.

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