Spectral K-way ratio-cut partitioning and clustering
DAC '93 Proceedings of the 30th international Design Automation Conference
Recent directions in netlist partitioning: a survey
Integration, the VLSI Journal
Spectral partitioning: the more eigenvectors, the better
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
A random graph model for massive graphs
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A spectral method to separate disconnected and nearly-disconnected web graph components
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Brief Application Description; Visual Data Mining: Recognizing Telephone Calling Fraud
Data Mining and Knowledge Discovery
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Monte-Carlo Algorithms for finding low-rank approximations
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
On clusterings-good, bad and spectral
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Fast Mining of Massive Tabular Data via Approximate Distance Computations
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
SVDPACK: A Fortran-77 Software Library for the Sparse Singular Value Decomposition
SVDPACK: A Fortran-77 Software Library for the Sparse Singular Value Decomposition
Clustering Large Graphs via the Singular Value Decomposition
Machine Learning
Structure and evolution of blogspace
Communications of the ACM - The Blogosphere
A divide-and-merge methodology for clustering
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
To randomize or not to randomize: space optimal summaries for hyperlink analysis
Proceedings of the 15th international conference on World Wide Web
Improved Approximation Algorithms for Large Matrices via Random Projections
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
On the structural properties of massive telecom call graphs: findings and implications
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
A novel evolutionary data mining algorithm with applications to churn prediction
IEEE Transactions on Evolutionary Computation
Multiway partitioning via geometric embeddings, orderings, and dynamic programming
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
KDD Cup 2007 task 1 winner report
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
WebKDD/SNAKDD 2007: web mining and social network analysis post-workshop report
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Community mining on dynamic weighted directed graphs
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Community-based greedy algorithm for mining top-K influential nodes in mobile social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
SOHAC: efficient storage of tick data that supports search and analysis
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
WAW'12 Proceedings of the 9th international conference on Algorithms and Models for the Web Graph
On spectral partitioning of co-authorship networks
CISIM'12 Proceedings of the 11th IFIP TC 8 international conference on Computer Information Systems and Industrial Management
Hi-index | 0.00 |
We evaluate various heuristics for hierarchical spectral clustering in large telephone call graphs. Spectral clustering without additional heuristics often produces very uneven cluster sizes or low quality clusters that may consist of several disconnected components, a fact that appears to be common for several data sources but, to our knowledge, not described in the literature. Divide-and-Merge, a recently described postfiltering procedure may be used to eliminate bad quality branches in a binary tree hierarchy. We propose an alternate solution that enables k-way cuts in each step by immediately filtering unbalanced or low quality clusters before splitting them further. Our experiments are performed on graphs with various weight and normalization built based on call detail records. We investigate a period of eight months of more than two millions of Hungarian landline telephone users. We measure clustering quality both by cluster ratio as well as by the geographic homogeneity of the clusters obtained from telephone location data. Although divide-and-merge optimizes its clusters for cluster ratio, our method produces clusters of similar ratio much faster, furthermore we give geographically much more homogeneous clusters with the size distribution of our clusters resembling to that of the settlement structure.