ABSTRACT
Affinity Propagation is a clustering algorithm used in many applications. It iteratively updates messages between data points until convergence. The message updating process enables Affinity Propagation to have higher clustering quality compared with other approaches. However, its computation cost is high; it is quadratic in the number of data points. This is because it updates the messages of all data point pairs. This paper proposes an efficient algorithm that guarantees the same clustering results as the original algorithm. Our approach, F-AP, is based on two ideas: (1) it computes upper and lower estimates to limit the messages to be updated in each iteration, and (2) it dynamically detects converged messages to efficiently skip unneeded updates. Experiments show that F-AP is much faster than previous approaches with no loss in clustering performance.
- K. Bache and M. Lichman. UCI Machine Learning Repository, 2013.Google Scholar
- D. Cai, X. Wang, and X. He. Probabilistic Dyadic Data Analysis with Local and Global Consistency. In ICML, pages 105--112, 2009. Google ScholarDigital Library
- J. T. Dudley, T. Deshpande, and A. J. Butte. Exploiting Drug Disease Relationships for Computational Drug Repositioning. Briefings in Bioinformatics, 12(4):303--311, 2011.Google ScholarCross Ref
- B. J. Frey and D. Dueck. Clustering by Passing Messages between Data Points. Science, 315:972--976, 2007.Google ScholarCross Ref
- Y. Fujiwara and G. Irie. Efficient Label Propagation. In ICML, pages 784--792, 2014.Google ScholarDigital Library
- Y. Fujiwara, G. Irie, and T. Kitahara. Fast Algorithm for Affinity Propagation. In IJCAI, pages 2238--2243, 2011. Google ScholarDigital Library
- Y. Fujiwara, G. Irie, S. Kuroyama, and M. Onizuka. Scaling Manifold Ranking Based Image Retrieval. PVLDB, 8(4):341--352, 2014. Google ScholarDigital Library
- D. S. Gunderson. Handbook of Mathematical Induction: Theory and Applications. Chapman and Hall/CRC, 2010. Google ScholarDigital Library
- J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2011. Google ScholarDigital Library
- R. Hu, B. M. Namee, and S. J. Delany. Off to a Good Start: Using Clustering to Select the Initial Training set in active learning. In FLAIRS, 2010.Google Scholar
- Y. Ida, T. Nakamura, and T. Matsumoto. Domain-dependent/independent Topic Switching Model for Online Reviews with Numerical Ratings. In CIKM, pages 229--238, 2013. Google ScholarDigital Library
- Y. Jia, J. Wang, C. Zhang, and X.-S. Hua. Finding Image Exemplars Using Fast Sparse Affinity Propagation. In ACM MM, pages 639--642, 2008. Google ScholarDigital Library
- F. R. Kschischang, B. J. Frey, and H. Loeliger. Factor Graphs and the Sum-product Algorithm. IEEE Transactions on Information Theory, 47(2):498--519, 2001. Google ScholarDigital Library
- N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar. Attribute and Simile Classifiers for Face Verification. In ICCV, pages 365--372, 2009.Google ScholarCross Ref
- J. Leskovec, A. Rajaraman, and J. D. Ullman. Mining of Massive Datasets. Cambridge University Press, 2014. Google ScholarDigital Library
- C. D. Manning, P. Raghavan, and H. Schutz. Introduction to Information Retrieval. Cambridge University Press, 2008. Google ScholarCross Ref
- M. Nakatsuji and Y. Fujiwara. Linked Taxonomies to Capture Users' Subjective Assessments of Items to Facilitate Accurate Collaborative Filtering. Artif. Intell., 207:52--68, 2014. Google ScholarDigital Library
- M. Nakatsuji, Y. Fujiwara, H. Toda, H. Sawada, J. Zheng, and J. A. Hendler. Semantic Data Representation for Improving Tensor Factorization. In AAAI, pages 2004--2012, 2014.Google ScholarDigital Library
- A. Rangrej, S. Kulkarni, and A. V. Tendulkar. Comparative Study of Clustering Techniques for Short Text Documents. In WWW, pages 111--112, 2011. Google ScholarDigital Library
- F. Shang, L. C. Jiao, J. Shi, F. Wang, and M. Gong. Fast Affinity Propagation Clustering: A Multilevel Approach. Pattern Recognition, 45(1):474--486, 2012. Google ScholarDigital Library
- H. Shiokawa, Y. Fujiwara, and M. Onizuka. Fast Algorithm for Modularity-based Graph Clustering. In AAAI, 2013.Google ScholarDigital Library
- H. Shiokawa, Y. Fujiwara, and M. Onizuka. SCAN : Efficient Algorithm for Finding Clusters, Hubs and Outliers on Large-scale Graphs. PVLDB, 8(11), 2015. Google ScholarDigital Library
- S. W. Smith. The Scientist & Engineer's Guide to Digital Signal Processing. California Technical Pub, 1997. Google ScholarDigital Library
- M. Toyoda, Y. Sakurai, and Y. Ishikawa. Pattern Discovery in Data Streams under the Time Warping Distance. VLDB J., 22(3):295--318, 2013. Google ScholarDigital Library
- J. Vlasblom and S. J. Wodak. Markov Clustering versus Affinity Propagation for the Partitioning of Protein Interaction Graphs. BMC Bioinformatics, 10, 2009.Google Scholar
- P. S. Yu, J. Han, and C. Faloutsos. Link Mining: Models, Algorithms, and Applications. Springer, 2010. Google ScholarDigital Library
- Z.-J. Zha, L. Yang, T. Mei, M. Wang, and Z. Wang. Visual Query Suggestion. In ACM MM, pages 15--24, 2009. Google ScholarDigital Library
- X. Zhang and J. C. Lv. Sparse Affinity Propagation for Image Analysis. JSW, 9(3):748--756, 2014.Google Scholar
- X. Zhang, W. Wang, K. Nørvåg, and M. Sebag. K-AP: Generating Specified K Clusters by Efficient Affinity Propagation. In ICDM, pages 1187--1192, 2010. Google ScholarDigital Library
Index Terms
- Adaptive Message Update for Fast Affinity Propagation
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