Transient Stability Multi Swing Step-out Prediction with Online Data Mining

2016 ◽  
Vol 136 (2) ◽  
pp. 137-144 ◽  
Author(s):  
Takuya Omi ◽  
Hiroto Kakisaka ◽  
Shinichi Iwamoto
Keyword(s):  
Data Mining ◽  
Transient Stability ◽  
Online Data

Online Data Mining for Co-Evolving Time Sequences

1999 ◽  
Author(s):  
B. K. Yi ◽  
N. D. Sidiropoulos ◽  
T. Johnson ◽  
H. V. Jagadish ◽  
C. Faloutsos
Keyword(s):  
Data Mining ◽  
Online Data ◽  
Time Sequences

Characterizing Intelligent Intrusion Detection and Prevention Systems Using Data Mining

2014 ◽  
pp. 89-102
Author(s):  
Mrutyunjaya Panda ◽  
Manas Ranjan Patra
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Computer Networks ◽  
Time Data ◽  
Security Risks ◽  
Security Systems ◽  
Online Data ◽  
Audit Data ◽  
Using Data ◽  
Intrusion Detection And Prevention

Intrusion Detection and Prevention Systems (IDPS) are being widely implemented to prevent suspicious threats in computer networks. Intrusion detection and prevention systems are security systems that are used to detect and prevent security threats to computer networks. In order to understand the security risks and IDPS, in this chapter, the authors make a quick review on classification of the IDPSs and categorize them in certain groups. Further, in order to improve accuracy and security, data mining techniques have been used to analyze audit data and extract features that can distinguish normal activities from intrusions. Experiments have been conducted for building efficient intrusion detection and prevention systems by combining online detection and offline data mining. During online data examination, real-time data are captured and are passed through a detection engine that uses a set of rules and parameters for analysis. During offline data mining, necessary knowledge is extracted about the process of intrusion.

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.

Critical and Future Trends in Data Mining

Data Mining ◽  
2011 ◽  
pp. 437-452 ◽  
Author(s):  
Jeffrey Hsu
Keyword(s):  
Data Mining ◽  
Business Education ◽  
Scientific Community ◽  
World Wide ◽  
Data Sources ◽  
Knowledge Discovery In Databases ◽  
Future Trends ◽  
Online Data ◽  
Usable Knowledge ◽  
The World

Every day, enormous amounts of information are generated from all sectors, whether it be business, education, the scientific community, the World Wide Web (WWW), or one of many readily available off-line and online data sources. From all of this, which represents a sizable repository of data and information, it is possible to generate worthwhile and usable knowledge. As a result, the field of Data Mining (DM) and knowledge discovery in databases (KDD) has grown in leaps and bounds and has shown great potential for the future (Han & Kamber, 2001). The purpose of this chapter is to survey many of the critical and future trends in the field of DM, with a focus on those which are thought to have the most promise and applicability to future DM applications.

Online Data Mining

2008 ◽  
pp. 75-83
Author(s):  
He´ctor Oscar Nigro ◽  
Sandra Elizabeth González Císaro
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Online Analytical Processing ◽  
Intelligent Data Analysis ◽  
Online Data ◽  
Analytical Processing

Several approaches for intelligent data analysis are not only available but also tried and tested. Online analytical processing (OLAP) and data mining represent two of the most important approaches. They mainly emphasize different aspects of the data and allow deriving of different kinds of information. So far, these approaches have mainly been used in isolation (Schwarz, 2002).

Online Data Mining

2005 ◽  
pp. 427-432
Author(s):  
Héctor Oscar Nigro ◽  
Sandra Elizabeth González Císaro
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Online Analytical Processing ◽  
Intelligent Data Analysis ◽  
Online Data ◽  
Analytical Processing

Several approaches for intelligent data analysis are not only available but also tried and tested. Online analytical processing (OLAP) and data mining represent two of the most important approaches. They mainly emphasize different aspects of the data and allow deriving of different kinds of information. So far, these approaches have mainly been used in isolation (Schwarz, 2002).

Data mining: an ethical baseline for online privacy policies

2016 ◽  
Vol 29 (4) ◽  
pp. 482-504 ◽  
Author(s):  
Matthew D Dean ◽  
Dinah M Payne ◽  
Brett J.L. Landry
Keyword(s):  
Data Mining ◽  
Code Of Conduct ◽  
Ethical Conduct ◽  
Online Privacy ◽  
Privacy Rights ◽  
Privacy Policies ◽  
Online Data ◽  
Content Type ◽  
Moral Principles ◽  
Thoughtful Discussion

Purpose – The purpose of this paper is to advocate for and provide guidance for the development of a code of ethical conduct surrounding online privacy policies, including those concerning data mining. The hope is that this research generates thoughtful discussion on the issue of how to make data mining more effective for the business stakeholder while at the same time making it a process done in an ethical way that remains effective for the consumer. The recognition of the privacy rights of data mining subjects is paramount within this discussion. Design/methodology/approach – The authors derive foundational principles for ethical data mining. First, philosophical literature on moral principles is used as the theoretical foundation. Then, using existing frameworks, including legislation and regulations from a range of jurisdictions, a compilation of foundational principles was derived. This compilation was then evaluated and honed through the integration of stakeholder perspective and the assimilation of moral and philosophical precepts. Evaluating a sample of privacy policies hints that current practice does not meet the proposed principles, indicating a need for changes in the way data mining is performed. Findings – A comprehensive framework for the development a contemporary code of conduct and proposed ethical practices for online data mining was constructed. Research limitations/implications – This paper provides a configuration upon which a code of ethical conduct for performing data mining, tailored to meet the particular needs of any organization, can be designed. Practical implications – The implications of data mining, and a code of ethical conduct regulating it, are far-reaching. Implementation of such principles serve to improve consumer and stakeholder confidence, ensure the enduring compliance of data providers and the integrity of its collectors, and foster confidence in the security of data mining. Originality/value – Existing legal mandates alone are insufficient to properly regulate data mining, therefore supplemental reference to ethical considerations and stakeholder interest is required. The adoption of a functional code of general application is essential to address the increasing proliferation of apprehension regarding online privacy.

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