Temporal Association Rule Mining in Event Sequences

Temporal Event Sequence Rule Mining

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch294 ◽

2011 ◽

pp. 1923-1928

Author(s):

Sherri K. Harms

Keyword(s):

Association Rule ◽

Association Rule Mining ◽

Temporal Data ◽

Rule Mining ◽

Event Sequences ◽

Use Of Data ◽

History Of ◽

Mining Methods ◽

History Of Use ◽

Sequence Rule

The emergence of remote sensing, scientific simulation and other survey technologies has dramatically enhanced our capabilities to collect temporal data. However, the explosive growth in data makes the management, analysis, and use of data both difficult and expensive. Methods that characterize interesting or unusual patterns from the volumes of temporal data are needed (Roddick & Spiliopoulou, 2002; Han & Kamber, 2005). The association rule mining methods described in this chapter provide the ability to find periodic occurrences of inter-sequential factors of interest, from groups of long, non-transactional temporal event sequences. Association rule mining is well-known to work well for problems related to the recognition of frequent patterns of data (Han & Kamber, 2005). Rules are relatively easy for humans to interpret and have a long history of use in artificial intelligence for representing knowledge learned from data.

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Integrating Remote Sensing into Wildlife Monitoring for Conservation

Environmental Conservation ◽

10.1017/s0376892919000092 ◽

2019 ◽

Vol 46 (03) ◽

pp. 181-183 ◽

Cited By ~ 5

Author(s):

PJ Stephenson

Keyword(s):

Remote Sensing ◽

Wildlife Conservation ◽

Time Series Data ◽

Decision Makers ◽

Series Data ◽

User Needs ◽

Wildlife Monitoring ◽

Use Of Data ◽

Technological Advances ◽

Local User

SummaryEffective wildlife monitoring is a prerequisite for effective wildlife conservation since, without time-series data on species populations and threats, evidence-based adaptive management will be difficult to achieve. Technological advances in remote sensing offer more opportunities for data collection than ever before. However, if we are to enhance data sharing and the use of data by decision-makers, methods must be relevant to local user needs and be integrated into monitoring schemes with appropriate goals and indicators.

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Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-019-1582-2 ◽

2019 ◽

Vol 2019 (1) ◽

Cited By ~ 2

Author(s):

Shadi Aljawarneh ◽

Aurea Anguera ◽

John William Atwood ◽

Juan A. Lara ◽

David Lizcano

Keyword(s):

Data Mining ◽

Time Series ◽

Knowledge Discovery ◽

Time Series Data ◽

Medical Patient ◽

Lessons Learned ◽

Physiological Signals ◽

Knowledge Discovery In Databases ◽

Series Data ◽

Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

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