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Frequency-based views to pattern collections

https://doi.org/10.1016/j.dam.2005.08.005

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Relational Data Mining

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On Supporting Interactive Association Rule Mining

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Performance Guarantees for Hierarchical Clustering

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Local and Global Methods in Data Mining: Basic Techniques and Open Problems

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Frequent Closures as a Concise Representation for Binary Data Mining

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Mining All Non-derivable Frequent Itemsets

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Pattern Detection and Discovery

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Relational Association Rules: Getting Warmer

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On the Complexity of Generating Maximal Frequent and Minimal Infrequent Sets

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Discovering Frequent Closed Itemsets for Association Rules

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The Elements of Statistical Learning

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The Elements of Statistical Learning: Data Mining, Inference, and Prediction

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A Theory of Inductive Query Answering

inferredcites →https://doi.org/10.1007/978-3-540-24775-3_58openalex-snapshot

Separating Structure from Interestingness

inferredcites →https://doi.org/10.1007/978-3-540-39644-4_19openalex-snapshot

Improving Efficiency of Frequent Query Discovery by Eliminating Non-relevant Candidates

inferredcites →https://doi.org/10.1007/978-3-540-39644-4_20openalex-snapshot

Chaining Patterns

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Discovering Unbounded Episodes in Sequential Data

inferredcites →https://doi.org/10.1007/978-3-540-39804-2_30openalex-snapshot

The Pattern Ordering Problem

inferredcites →https://doi.org/10.1007/978-3-540-39804-2_8openalex-snapshot

ExAnte: Anticipated Data Reduction in Constrained Pattern Mining

inferredcites →https://doi.org/10.1007/978-3-540-39804-2_9openalex-snapshot

Minimal k-Free Representations of Frequent Sets

inferredcites →https://doi.org/10.1007/978-3-662-04599-2openalex-snapshot

Relational Data Mining

inferredcites →https://doi.org/10.1007/978-3-662-04599-2_8openalex-snapshot

Discovery of Relational Association Rules

inferredcites →https://doi.org/10.1007/b13634openalex-snapshot

Knowledge Discovery in Databases: PKDD 2003

inferredcites →https://doi.org/10.1007/bf01271266openalex-snapshot

Inclusion-exclusion: Exact and approximate

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How Fast Is the k-Means Method?

inferredcites →https://doi.org/10.1007/s10115-004-0174-5openalex-snapshot

Reliable detection of episodes in event sequences

inferredcites →https://doi.org/10.1016/b978-012722442-8/50011-2openalex-snapshot

The History of Histograms (abridged)

inferredcites →https://doi.org/10.1016/j.comgeo.2004.03.003openalex-snapshot

A local search approximation algorithm for k-means clustering

inferredcites →https://doi.org/10.1016/j.jcss.2004.10.006openalex-snapshot

Performance guarantees for hierarchical clustering

inferredcites →https://doi.org/10.1016/s0169-023x(02)00057-5openalex-snapshot

Computing iceberg concept lattices with Titanic

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Advances in knowledge discovery and data mining

inferredcites →https://doi.org/10.1016/s0933-3657(02)00058-1openalex-snapshot

Principles of Data Mining

inferredcites →https://doi.org/10.1017/s0269888904220203openalex-snapshot

Principles of Data Mining by David J. Hand, Heikki Mannila and Padhraic Smyth, MIT Press, 546 pp., £34.50, ISBN 0-262-08290-X

inferredcites →https://doi.org/10.1023/a:1007652502315openalex-snapshot

SPADE: An Efficient Algorithm for Mining Frequent Sequences

inferredcites →https://doi.org/10.1023/a:1009748302351openalex-snapshot

Discovery of Frequent Episodes in Event Sequences

inferredcites →https://doi.org/10.1023/a:1009796218281openalex-snapshot

Levelwise Search and Borders of Theories in Knowledge Discovery

inferredcites →https://doi.org/10.1023/a:1009895914772openalex-snapshot

Constraint-Based Rule Mining in Large, Dense Databases

inferredcites →https://doi.org/10.1023/a:1021571501451openalex-snapshot

Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries

inferredcites →https://doi.org/10.1023/a:1021726221443openalex-snapshot

Complete Mining of Frequent Patterns from Graphs: Mining Graph Data

inferredcites →https://doi.org/10.1023/b:dami.0000005258.31418.83openalex-snapshot

Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

inferredcites →https://doi.org/10.1080/01621459.1958.10501479openalex-snapshot

On Grouping for Maximum Homogeneity

inferredcites →https://doi.org/10.1109/69.149926openalex-snapshot

An information theoretic approach to rule induction from databases

inferredcites →https://doi.org/10.1109/69.846291openalex-snapshot

Scalable algorithms for association mining

inferredcites →https://doi.org/10.1109/69.908981openalex-snapshot

Finding interesting associations without support pruning

inferredcites →https://doi.org/10.1109/focs.2004.7openalex-snapshot

A Simple Linear Time (1+ ∊) -Approximation Algorithm for k-Means Clustering in Any Dimensions

inferredcites →https://doi.org/10.1109/icde.2001.914856openalex-snapshot

Mining frequent itemsets with convertible constraints

inferredcites →https://doi.org/10.1109/icde.2004.1319986openalex-snapshot

BIDE: efficient mining of frequent closed sequences

inferredcites →https://doi.org/10.1109/icdm.2001.989514openalex-snapshot

Efficiently mining maximal frequent itemsets

inferredcites →https://doi.org/10.1109/icdm.2001.989533openalex-snapshot

Concise representation of frequent patterns based on disjunction-free generators

inferredcites →https://doi.org/10.1109/icdm.2001.989534openalex-snapshot

Frequent subgraph discovery

inferredcites →https://doi.org/10.1109/icdm.2002.1183894openalex-snapshot

A theory of inductive query answering

inferredcites →https://doi.org/10.1109/icdm.2002.1183911openalex-snapshot

Discovering frequent geometric subgraphs

inferredcites →https://doi.org/10.1109/icdm.2002.1183928openalex-snapshot

On computing condensed frequent pattern bases

inferredcites →https://doi.org/10.1109/icdm.2002.1184038openalex-snapshot

gSpan: graph-based substructure pattern mining

inferredcites →https://doi.org/10.1109/icdm.2003.1250904openalex-snapshot

Reliable detection of episodes in event sequences

inferredcites →https://doi.org/10.1109/icdm.2003.1250943openalex-snapshot

Efficient data mining for maximal frequent subtrees

inferredcites →https://doi.org/10.1109/tkde.2002.1019211openalex-snapshot

Finding patterns in three-dimensional graphs: algorithms and applications to scientific data mining

inferredcites →https://doi.org/10.1145/1007730.1007744openalex-snapshot

Advances in frequent itemset mining implementations

inferredcites →https://doi.org/10.1145/1081870.1081907openalex-snapshot

Summarizing itemset patterns

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Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

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Proceedings of the 1992 ACM SIGMOD international conference on Management of data

inferredcites →https://doi.org/10.1145/170035.170072openalex-snapshot

Mining association rules between sets of items in large databases

inferredcites →https://doi.org/10.1145/1807167openalex-snapshot

Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

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Beyond market baskets

inferredcites →https://doi.org/10.1145/276304.276313openalex-snapshot

Efficiently mining long patterns from databases

inferredcites →https://doi.org/10.1145/312129.312219openalex-snapshot

Mining the most interesting rules

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Proceedings of the 56th Annual ACM Symposium on Theory of Computing

inferredcites →https://doi.org/10.1145/375551.375604openalex-snapshot

A condensed representation to find frequent patterns

inferredcites →https://doi.org/10.1145/380995.381017openalex-snapshot

Mining frequent patterns with counting inference

inferredcites →https://doi.org/10.1145/502585.502600openalex-snapshot

Multi-dimensional sequential pattern mining

inferredcites →https://doi.org/10.1145/509907.509947openalex-snapshot

Approximate clustering via core-sets

inferredcites →https://doi.org/10.1145/513400.513402openalex-snapshot

A local search approximation algorithm for k-means clustering

inferredcites →https://doi.org/10.1145/568574.568580openalex-snapshot

Constrained frequent pattern mining

inferredcites →https://doi.org/10.1145/568574.568582openalex-snapshot

Is pushing constraints deeply into the mining algorithms really what we want?

inferredcites →https://doi.org/10.1145/62212.62255openalex-snapshot

Optimal algorithms for approximate clustering

inferredcites →https://doi.org/10.1145/773153.773180openalex-snapshot

How to quickly find a witness

inferredcites →https://doi.org/10.1145/775047.775053openalex-snapshot

Selecting the right interestingness measure for association patterns

inferredcites →https://doi.org/10.1145/775047.775058openalex-snapshot

Efficiently mining frequent trees in a forest

inferredcites →https://doi.org/10.1145/775047.775070openalex-snapshot

Pattern discovery in sequences under a Markov assumption

inferredcites →https://doi.org/10.1145/777943.777945openalex-snapshot

Discovering all most specific sentences

inferredcites →https://doi.org/10.1145/780542.780550openalex-snapshot

Approximation schemes for clustering problems

inferredcites →https://doi.org/10.1145/958942.958944openalex-snapshot

Efficient dynamic mining of constrained frequent sets

inferredcites →https://doi.org/10.1145/990308.990313openalex-snapshot

On clusterings

inferredcites →https://doi.org/10.1214/ecp.v7-1050openalex-snapshot

Inclusion-Exclusion Redux

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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A Proof of Beal’s Conjecture Using Pythagoras Perspective

inferred← citeshttps://doi.org/10.1007/11733492_9openalex-snapshot

Transaction Databases, Frequent Itemsets, and Their Condensed Representations

inferred← citeshttps://doi.org/10.1007/978-3-540-74205-0_51openalex-snapshot

New Classification Method Based on Support-Significant Association Rules Algorithm

inferred← citeshttps://doi.org/10.1016/j.dam.2006.05.003openalex-snapshot

On the stability properties of linear dynamic time-varying unforced systems involving switches between parameterizations from topologic considerations via graph theory

inferred← citeshttps://doi.org/10.1109/icdm.2003.1250923openalex-snapshot

Change profiles

inferred← citeshttps://doi.org/10.1109/qsic.2009.27openalex-snapshot

Supporting the Extraction of Timed Properties for Passive Testing by Using Probabilistic User Models

inferred← citeshttps://doi.org/10.3115/1273073.1273096openalex-snapshot

A bio-inspired approach for multi-word expression extraction

inferred← citeshttps://doi.org/10.48550/arxiv.cs/0505071openalex-snapshot

Summarization Techniques for Pattern Collections in Data Mining

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