CLARANS: A Method for Clustering Objects for Spatial Data Mining

By Raymond T. Ng, Jiawei Han. IEEE Transactions on Knowledge and Data Engineering, 2002

1,258 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/fd3010cdd7e92dad1d66b7a3da7b8bdf0a9679f8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ying-jie Tian
2. Dongkuan Xu
3. Chris Schwiegelshohn
4. Mia Hubert
5. Vipin Kumar
6. Rui Xu
7. Donald C. Wunsch II
8. Erich Schubert

In Information Systems

1. Chin-Teng Lin
2. Victor J. Rayward-Smith
3. Ying-jie Tian
4. Mia Hubert
5. Rui Xu
6. Donald C. Wunsch II
7. Marimuthu Swami Palaniswami
8. Alan P. Reynolds

In Signal Processing

1. Mia Hubert
2. Rui Xu
3. Donald C. Wunsch II
4. Anuj Karpatne
5. Chin-Teng Lin
6. Melanie Schmidt
7. Chris Schwiegelshohn
8. Peter J. Rousseeuw

Citations in the corpus

• BICO: BIRCH Meets Coresets for k-Means Clustering (2013, background): “There is a lot of work on related problems, for example CURE [18] which requires more than one pass over the data, DBSCAN [9] which is not center based, CLARANS [25] which is typically used when centers have to be chosen from the dataset and is not particularly optimized for points in Euclidean spac”
• A review of clustering techniques and developments (2017, methodology): “The algorithms [33] studied in this category include: k-means [38], PAM [173], CLARA [173], CLARANS [174], Fuzzy C-means, DBSCAN etc.”
• Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms (2006, methodology): “Section 8 presents four clustering algorithms, k-means [14], k-medoids, PAM [13] and CLARANS [15], that partition a set of objects into k clusters.”
• Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms (2018, methodology): “w. Wei, Lee, and Hsu [23] found the genetic methods to work only for small data sets, small k, and well separated symmetric clusters, and it was usually outperformed by CLARANS. The algorithm CLARANS [15] interprets the search space as a high-dimensional hypergraph, where each edge corresponds to sw”
• Data Mining for Internet of Things: A Survey (2014, background): “Digital Object Identifier 10.1109/SURV.2013.103013.00206 1Since no confusion is possible, throughout the rest of the paper, the terms “things,” “devices,” “objects,” and etc. will be used interchangably. smartness [9], [10].”
• Anomaly detection by robust statistics (2017, methodology): “The CLARA algorithm 57 implemented this method for large datasets, and was extended to CLARANS 58 for spatial data mining.”
• A Comprehensive Survey of Clustering Algorithms (2015, methodology): “The typical clustering algorithms based on partition also include PAM [9], CLARA [10], CLARANS [11].”
• Survey of clustering algorithms (2005, background): “3) Many novel algorithms have been developed to cluster large-scale data sets, especially in the context of data mining [44], [45], [85], [135], [213], [248].”
• Spatio-Temporal Data Mining (2017, methodology): “Approaches for clustering spatial objects such as CLARANS [Ng and Han 2002] can also be extended in the ST domain for finding groups of objects that are related in both space and time.”
• Fuzzy c-Means Algorithms for Very Large Data (2012, methodology): “Well-known algorithms of this type include BIRCH [27] and CLARANS [28].”