Algorithms for clustering data
Algorithms for clustering data
Models of incremental concept formation
Artificial Intelligence
The SEQUOIA 2000 storage benchmark
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Incremental clustering and dynamic information retrieval
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
ACM Computing Surveys (CSUR)
Data mining: concepts and techniques
Data mining: concepts and techniques
A Study on the Hierarchical Data Clustering Algorithm Based on Gravity Theory
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Cluster Analysis
Incremental and effective data summarization for dynamic hierarchical clustering
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Online Hierarchical Clustering in a Data Warehouse Environment
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Quality-Aware Sampling and Its Applications in Incremental Data Mining
IEEE Transactions on Knowledge and Data Engineering
Immune-inspired incremental feature selection technology to data streams
Applied Soft Computing
Quantization-based clustering algorithm
Pattern Recognition
Towards subspace clustering on dynamic data: an incremental version of PreDeCon
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
Density based subspace clustering over dynamic data
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Dynamic incremental data summarization for hierarchical clustering
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
Knowledge augmentation via incremental clustering: new technology for effective knowledge management
International Journal of Business Information Systems
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One of the main challenges in the design of modern clustering algorithms is that, in many applications, new data sets are continuously added into an already huge database. As a result, it is impractical to carry out data clustering from scratch whenever there are new data instances added into the database. One way to tackle this challenge is to incorporate a clustering algorithm that operates incrementally. Another desirable feature of clustering algorithms is that a clustering dendrogram is generated. This feature is crucial for many applications in biological, social, and behavior studies, due to the need to construct taxonomies. This paper presents the GRIN algorithm, an incremental hierarchical clustering algorithm for numerical data sets based on gravity theory in physics. The GRIN algorithm delivers favorite clustering quality and generally features O(n) time complexity. One main factor that makes the GRIN algorithm be able to deliver favorite clustering quality is that the optimal parameters settings in the GRIN algorithm are not sensitive to the distribution of the data set. On the other hand, many modern clustering algorithms suffer unreliable or poor clustering quality when the data set contains highly skewed local distributions so that no optimal values can be found for some global parameters. This paper also reports the experiments conducted to study the characteristics of the GRIN algorithm.