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149
Correlation Clustering with Noisy Input
"... Correlation clustering is a type of clustering that uses a basic form of input data: For every pair of data items, the input specifies whether they are similar (belonging to the same cluster) or dissimilar (belonging to different clusters). This information may be inconsistent, and the goal is to fi ..."
Abstract
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Cited by 23 (1 self)
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Correlation clustering is a type of clustering that uses a basic form of input data: For every pair of data items, the input specifies whether they are similar (belonging to the same cluster) or dissimilar (belonging to different clusters). This information may be inconsistent, and the goal
ADAPTIVE CLUSTER-DISTANCE BOUNDING FOR NEAREST NEIGHBOR SEARCH IN IMAGE DATABASES
"... We consider approaches for exact similarity search in a high dimensional space of correlated features representing image datasets, based on principles of clustering and vector quantization. We develop an adaptive cluster distance bound based on separating hyperplanes, that complements our index in s ..."
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Cited by 1 (0 self)
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We consider approaches for exact similarity search in a high dimensional space of correlated features representing image datasets, based on principles of clustering and vector quantization. We develop an adaptive cluster distance bound based on separating hyperplanes, that complements our index
ADAPTIVE CLUSTER-DISTANCE BOUNDING FOR NEAREST NEIGHBOR SEARCH IN IMAGE DATABASES
"... We consider approaches for exact similarity search in a high dimensional space of correlated features representing image datasets, based on principles of clustering and vector quantization. We develop an adaptive cluster distance bound based on separating hyperplanes, that complements our index in s ..."
Abstract
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We consider approaches for exact similarity search in a high dimensional space of correlated features representing image datasets, based on principles of clustering and vector quantization. We develop an adaptive cluster distance bound based on separating hyperplanes, that complements our index
Dynamically Optimizing Queries over Large Scale Data Platforms
"... ABSTRACT Enterprises are adapting large-scale data processing platforms, such as Hadoop, to gain actionable insights from their "big data". Query optimization is still an open challenge in this environment due to the volume and heterogeneity of data, comprising both structured and un/semi ..."
Abstract
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Cited by 1 (1 self)
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in this setting, while at the same time, suboptimal query plans can be disastrous with large datasets. In this paper, we propose new techniques that take into account UDFs and correlations between relations for optimizing queries running on large scale clusters. We introduce "pilot runs", which execute
Io-top-k: Index-access optimized top-k query processing
- In VLDB
, 2006
"... Top-k query processing is an important building block for ranked retrieval, with applications ranging from text and data integration to distributed aggregation of network logs and sensor data. Top-k queries operate on index lists for a query’s elementary conditions and aggregate scores for result ca ..."
Abstract
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Cited by 43 (3 self)
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, principled, scheduling methods based on a Knapsackrelated optimization for sequential accesses and a cost model for random accesses. The methods can be further boosted by harnessing probabilistic estimators for scores, selectivities, and index list correlations. In performance experiments with three
Do Query Optimizers Need to be SSD-aware?
"... Flash-based solid state disks (SSDs) are beginning to supplant conventional rotating disks for performance-critical data in myriad DBMS applications, including decision support systems. Though SSDs provide the same block-oriented storage abstraction as conventional disks, their performance character ..."
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Cited by 3 (2 self)
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non-clustered index scans more frequently). Surprisingly, our empirical investigation using a commercial DBMS finds it is not necessary to adjust query optimization when shifting relations from disk to flash—an SSD-oblivious optimizer generally makes effective choices. We make two main observations
Nonparametric function estimation for clustered data when the predictor is measured without/with error,
- Journal of the American Statistical Association,
, 2000
"... Abstract We consider local polynomial kernel regression with a single covariate for clustered data using estimating equations. We assume that at most m < ∞ observations are available on each cluster. In the case of random regressors, with no measurement error in the predictor, we show that it is ..."
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Cited by 70 (16 self)
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that it is generally the best strategy to ignore entirely the correlation structure within each cluster, and instead to pretend that all observations are independent. In the further special case of longitudinal data on individuals with fixed common observation times, we show that equivalent to the pooled data approach
An improved algorithm for bipartite correlation clustering. European Symposium on Algorithms
, 2011
"... Bipartite Correlation clustering is the problem of generating a set of disjoint bi-cliques on a set of nodes while minimizing the symmetric difference to a bipartite input graph. The number or size of the output clusters is not constrained in any way. The best known approximation algorithm for this ..."
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Cited by 2 (0 self)
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bipartite). The analysis for Correlation Clustering there required defining events for structures containing 3 vertices and using the probability of these events to produce a feasible solution to a dual of a certain natural LP bounding the optimal cost. It is tempting here to use sets of 4 vertices, which
Using the crowd for top-k and group-by queries
- In ICDT
, 2013
"... Group-by and top-k are fundamental constructs in database queries. However, the criteria used for grouping and ordering certain types of data – such as unlabeled photos clustered by the same person ordered by age – are difficult to evaluate by machines. In contrast, these tasks are easy for humans t ..."
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Cited by 21 (6 self)
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Group-by and top-k are fundamental constructs in database queries. However, the criteria used for grouping and ordering certain types of data – such as unlabeled photos clustered by the same person ordered by age – are difficult to evaluate by machines. In contrast, these tasks are easy for humans
Results 1 - 10
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149
