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Mining Peculiarity Groups in Day-by-Day Behavioral Datasets

https://doi.org/10.1109/icdm.2009.48
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16/16 checkable references clean · checked 2026-07-25

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

6 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 16 checked references that resolve
resolves10.1007/s10844-006-0006-z
Constraint-based sequential pattern mining: the pattern-growth methods
resolves10.1007/978-94-015-3994-4
Identification of Outliers
resolves10.1007/s00778-004-0125-5
An effective and efficient algorithm for high-dimensional outlier detection
resolves10.1109/TKDE.2005.31
Outlier mining in large high-dimensional data sets
resolves10.1109/ICDM.2004.10008
Relational Peculiarity Oriented Data Mining
resolves10.1007/s10618-006-0046-6
Relational peculiarity-oriented mining
resolves10.1145/1401890.1401983
Local peculiarity factor and its application in outlier detection
resolves10.1145/564736.564737
Clustering by pattern similarity in large data sets
resolves10.1145/1401890.1401974
Can complex network metrics predict the behavior of NBA teams?
resolves10.1109/ICDE.1995.380415
Mining sequential patterns
resolves10.1109/ICDM.2006.47
Data Mining Approaches to Criminal Career Analysis
resolves10.1109/TKDE.2007.190635
Mining Impact-Targeted Activity Patterns in Imbalanced Data
resolves10.1142/S0219622006002271
FROM DATA MINING TO BEHAVIOR MINING
resolves10.1145/564691.564738
Mining long sequential patterns in a noisy environment
resolves10.1137/1.9781611972740.9
A Top-Down Method for Mining Most Specific Frequent Patterns in Biological Sequence Data
resolves10.1145/276304.276313
Efficiently mining long patterns from databases
The 6 references without a DOI — listed, not checked
no DOI — not checkedExample-based robust outlier detection in high dimensional datasets
no DOI — not checkedMaPle: a fast algorithm for maximal pattern-based clustering
no DOI — not checkedMining unexpected Web usage behaviors
no DOI — not checkedPrefixSpan: mining sequential patterns efficiently by prefixprojected pattern growth
no DOI — not checkedref7
no DOI — not checkedA multi-supports-based sequential pattern mining algorithm
What this badge says. CiteStamped means the CHECKABLE references of this work were clean at the dated check: each resolved to a known work in a public registry, and none carried a retraction notice at that time. It says nothing about the quality, findings, or importance of the work itself, and nothing about references deposited without a DOI.

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