K. Murthy

Publications37
h-index13
Citations1,433
Highly Influential Citations135
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  • Publications
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Density-Connected Subspace Clustering for High-Dimensional Data
TLDR
We introduce SUBCLU (density-connected Subspace Clustering), an effective and efficient approach to the subspace clustering problem. Expand
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Density connected clustering with local subspace preferences
TLDR
In this paper, we introduce the concept of local subspace preferences, which captures the main directions of high point density. Expand
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Computing Clusters of Correlation Connected objects
TLDR
In this paper, we propose a method called 4C (Computing Correlation Connected Clusters) to identify local subgroups of the data objects sharing a uniform but arbitrarily complex correlation. Expand
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Subspace selection for clustering high-dimensional data
TLDR
We present a feature selection technique called SURFING (subspaces relevant for clustering) that finds all relevant subspaces in high dimensional, sparse data sets and sorts them by relevance and produces better results than comparative methods. Expand
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DUST: a generalized notion of similarity between uncertain time series
TLDR
We propose a novel distance measure that accommodates uncertainty and degenerates to the Euclidean distance when the distance is large compared to the error. Expand
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Efficient Similarity Search for Hierarchical Data in Large Databases
TLDR
We present a set of new filter methods for structural and for content-based information in tree-structured data as well as ways to flexibly combine different filter criteria. Expand
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Towards Traceability across Sovereign, Distributed RFID Databases
TLDR
In this paper, we introduce the formal concept of traceability networks and highlight the technical challenges involved in sharing data in such a network. Expand
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Clustering Multi-represented Objects with Noise
TLDR
We present an efficient density-based approach to cluster multi-represented data, taking all available representations into account. Expand
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Ranking Interesting Subspaces for Clustering High Dimensional Data
TLDR
We present a pre-processing step for traditional clustering algorithms, which detects all interesting subspaces of high-dimensional data containing clusters. Expand
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Discovery Services-Enabling RFID Traceability in EPCglobal Networks
TLDR
The EPCglobal consortium defines standards to enable data sharing of electronic product code related information within and between enterprises, which typically comprises events of RFID readers as well as product information. Expand
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