Algorithms for clustering data
Algorithms for clustering data
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A survey of data mining and knowledge discovery software tools
ACM SIGKDD Explorations Newsletter
| Hi-index | 0.00 |
Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. Unsupervised learning (clustering) deals with which have not been pre classified in any way and so do not have a class attribute associated with them. The scope of applying clustering algorithm is to discover useful but unknown classes of items. Unsupervised learning is an approach of learning where instances are automatically placed into meaningful groups based on their similarity. This paper addresses fundamental concepts of unsupervised learning while it serveys recent clustering algorithm and their complexities.