The electrical resistance of a graph captures its commute and cover times
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering categorical data: an approach based on dynamical systems
The VLDB Journal — The International Journal on Very Large Data Bases
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Hierarchical clustering of mixed data based on distance hierarchy
Information Sciences: an International Journal
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Schema mapping verification: the spicy way
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph nodes clustering with the sigmoid commute-time kernel: A comparative study
Data & Knowledge Engineering
Mutual Information Based Extrinsic Similarity for Microarray Analysis
BICoB '09 Proceedings of the 1st International Conference on Bioinformatics and Computational Biology
TANGENT: a novel, 'Surprise me', recommendation algorithm
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
DISC: data-intensive similarity measure for categorical data
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Attribute value weighting in k-modes clustering
Expert Systems with Applications: An International Journal
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Similarity or distance measures are fundamental and critical properties for data mining tools. Categorical attributes abound in databases. The Car Make, Gender, Occupation, etc. fields in a automobile insurance database are very informative. Sadly, categorical data is not easily amenable to similarity computations. A domain expert might manually specify some or all of the similarity relationships, but this is error-prone and not feasible for attributes with large domains, nor is it useful for cross-attribute similarities, such as between Gender and Occupation. External similarity functions define a similarity between, say, Car Makes by looking at how they co-occur with the other categorical attributes. We exploit a rich duality between random walks on graphs and electrical circuits to develop REP, an external similarity function. REP is theoretically grounded while the only prior work was ad-hoc. The usefulness of REP is shown in two experiments. First, we cluster categorical attribute values showing improved inferred relationships. Second, we use REP effectively as a nearest neighbour classifier.