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SDM
2004
SIAM

271views Data Mining» more SDM 2004»

A Foundational Approach to Mining Itemset Utilities from Databases

14 years 24 days ago

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Most approaches to mining association rules implicitly consider the utilities of the itemsets to be equal. We assume that the utilities of itemsets may differ, and identify the hi...

Hong Yao, Howard J. Hamilton, Cory J. Butz

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Id	Views	Title	Status

1	271	A Foundational Approach to Mining Itemset Utilities from Databases	claim
2	253	Density-Connected Subspace Clustering for High-Dimensional Data	claim
3	242	Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification	claim
4	229	R-MAT: A Recursive Model for Graph Mining	claim
5	225	Active Semi-Supervision for Pairwise Constrained Clustering	claim
6	224	Hierarchical Clustering for Thematic Browsing and Summarization of Large Sets of Association Rules	claim
7	218	Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from Data	claim
8	214	Making Time-Series Classification More Accurate Using Learned Constraints	claim
9	212	Clustering with Bregman Divergences	claim
10	211	Using Support Vector Machines for Classifying Large Sets of Multi-Represented Objects	claim
11	207	BAMBOO: Accelerating Closed Itemset Mining by Deeply Pushing the Length-Decreasing Support Constraint	claim
12	194	Finding Frequent Patterns in a Large Sparse Graph	claim
13	189	An Abstract Weighting Framework for Clustering Algorithms	claim
14	187	Minimum Sum-Squared Residue Co-Clustering of Gene Expression Data	claim
15	187	Class-Specific Ensembles for Active Learning	claim
16	177	Reservoir-Based Random Sampling with Replacement from Data Stream	claim
17	174	Classifying Documents Without Labels	claim
18	168	GenIc: A Single-Pass Generalized Incremental Algorithm for Clustering	claim
19	166	A Framework for Discovering Co-Location Patterns in Data Sets with Extended Spatial Objects	claim
20	165	Visualizing RFM Segmentation	claim
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22	162	Subspace Clustering of High Dimensional Data	claim
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25	141	Active Mining of Data Streams	claim
26	141	Visually Mining through Cluster Hierarchies	claim
27	123	Nonlinear Manifold Learning for Data Stream	claim

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