Knowledge discovery in an earthquake text database: correlation between significant earthquakes and the time of day

Goldman, James; Parker, D. Stott; Chu, Wesley W.

doi:10.1109/ssdm.1997.621144

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…To validate this discovery, we compared our observed distribution against that of other patient data reported in the literature. Using the data reported in the medical literature, a 2 -test confirmed that our distribution is highly unlikely to be the result of random observations. It should be noted that the majority of the literature we reviewed had an entirely different focus.…”

Section: Introductionsupporting

confidence: 61%

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Term Domain Distribution Analysis: a Data Mining Tool for Text Databases

Goldman¹,

Chu²,

Parker³

et al. 1999

Methods Inf Med

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In this paper, we give a case history illustrating the real-world application of a useful technique for data mining of text databases. The technique, which we call Term Domain Distribution Analysis (TDDA), consists of keeping track of term frequencies for specific finite domains and announcing significant differences from standard frequency distributions over these domains as a hypothesis. TDDA is part of a larger framework, the Digital Filter Model, for data mining of text documents. In the case study presented, the domain of terms was the pair {right, left}, over which we expected a uniform distribution. In analyzing term frequencies in a thoracic lung cancer database, the TDDA technique led to the surprising discovery that primary thoracic lung cancer tumors appear in the right lung more often than the left lung, with a ratio of 3:2. Treating the text discovery as a hypothesis, we verified this relationship against the medical literature in which primary lung tumor sites were reported, using a standard χ2 statistic. We subsequently developed a working theoretical model of lung cancer that may explain the discovery. This discovery and our model may change how oncologists view the mechanisms of primary lung tumor location.

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Section: Introductionsupporting

confidence: 61%

“…Text-based knowledge discovery systems [1][2][3][4][5] are just now beginning to emerge, although Information Retrieval (IR) systems have been developed for 30 years [6][7][8]. In IR, the best retrieval and routing techniques do a respectable job at ranking relevant documents to a given query [6].…”

Section: Introductionmentioning

confidence: 99%

Term Domain Distribution Analysis: a Data Mining Tool for Text Databases

Goldman¹,

Chu²,

Parker³

et al. 1999

Methods Inf Med

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show abstract

“…Previous studies [3] conducted in the California region showed that earthquakes occur more frequently in morning hours. We made a similar analysis for the Vrancea region.…”

Section: Data Preprocessing Basic Statisticsmentioning

confidence: 93%

Using Data Mining Techniques for the Management of Seismic Vulnerability

Leon

Atanasiu

Gâlea³

2006

KEM

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Natural hazards, and especially earthquakes, are often recurring phenomena. Therefore, there is a permanent need for solutions to reduce earthquake losses by developing technologies, procedures, knowledge, and tools for seismic design and rehabilitation of buildings and infrastructure. A key point to an effective decision making process that aims at mitigating their effects is building a model of the underlying facts. A Geographical Information System (GIS) is a framework able to assemble, keep, process and display specific information, identified by geographical location, which can combine layers of information to give the user a better understanding about that location. By using a Geographical Information System containing geospatial data, one can develop useful scenarios to reduce natural disaster risk and vulnerability of structures. In this paper, we describe a way of applying data mining techniques from the artificial intelligence field to earthquake analysis in order to make a better investigation of the available data. These methods are capable of finding “hidden” correlations among different subsets of data, which cannot be revealed by means of simple statistics.

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Local time dependence of earthquakes occurrence and its possible connection with geomagnetic diurnal variations

Takla

2022

NRIAG Journal of Astronomy and Geophysics

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Knowledge discovery in an earthquake text database: correlation between significant earthquakes and the time of day

Cited by 3 publications

References 17 publications

Term Domain Distribution Analysis: a Data Mining Tool for Text Databases

Term Domain Distribution Analysis: a Data Mining Tool for Text Databases

Using Data Mining Techniques for the Management of Seismic Vulnerability

Local time dependence of earthquakes occurrence and its possible connection with geomagnetic diurnal variations

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