Micheline Kamber

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dblp:97/3517 · DBLP profile ↗

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3ranked-venue papers

3as first author

0since 2021 · last 1997

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 first-authorDatabases, data management, data science and information retrieval · 2 · 2 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval

2 papers

Data mining · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Data mining › pattern mining association rule mining	2	1997	Metarule-Guided Mining of Multi-Dimensional Association Rules Using Data Cubes · KDD 1997 Evaluating the Interestingness of Characteristic Rules · KDD 1996
Data mining › pattern mining › association rule mining multidimensional association rule mining	1	1997	Metarule-Guided Mining of Multi-Dimensional Association Rules Using Data Cubes · KDD 1997
Data mining › pattern mining › association rule mining interestingness measures	1	1996	Evaluating the Interestingness of Characteristic Rules · KDD 1996

Methods — techniques the papers use, named apart from their topics

data cube · 0.0

Year	Publication	Venue	Position
1997	Metarule-Guided Mining of Multi-Dimensional Association Rules Using Data Cubes Micheline Kamber, Jiawei Han 0001, Jenny Chiang	KDD	1
1996	Evaluating the Interestingness of Characteristic Rules Micheline Kamber, Rajjan Shinghal	KDD	1
1995	Model-based 3-D segmentation of multiple sclerosis lesions in magnetic resonance brain images abstract Human investigators instinctively segment medical images into their anatomical components, drawing upon prior knowledge of anatomy to overcome image artifacts, noise, and lack of tissue contrast. The authors describe: 1) the development and use of a brain tissue probability model for the segmentation of multiple sclerosis (MS) lesions in magnetic resonance (MR) brain images, and 2) an empirical comparison of the performance of statistical and decision tree classifiers, applied to MS lesion segmentation. Based on MR image data obtained from healthy volunteers, the model provides prior probabilities of brain tissue distribution per unit voxel in a standardized 3-D "brain space". In comparison to purely data-driven segmentation, the use of the model to guide the segmentation of MS lesions reduced the volume of false positive lesions by 50-80% Micheline Kamber, Rajjan Shinghal, D. Louis Collins, Gordon S. Francis, Alan C. Evans	IEEE Trans. Medical Imaging	1