The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Self-organizing maps
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Data mining: concepts and techniques
Data mining: concepts and techniques
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Data Mining of Software Development Databases
Software Quality Control
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
A Comparison of Several Approaches to Missing Attribute Values in Data Mining
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
An Incremental Hierarchical Data Clustering Algorithm Based on Gravity Theory
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A survey of data mining and knowledge discovery software tools
ACM SIGKDD Explorations Newsletter
Clustering Large Datasets in Arbitrary Metric Spaces
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Data Clustering Using Evidence Accumulation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
k-means: a new generalized k-means clustering algorithm
Pattern Recognition Letters
Finding natural clusters using multi-clusterer combiner based on shared nearest neighbors
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Hi-index | 0.00 |
Learning paradigm is associated with the study of how computers and natural systems such as humans learn in the presence of both labelled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled or in the supervised paradigm (e.g., classification, regression) where all the data are labelled. 'Incremental learning' is an approach to deal with classification task or clustering when datasets are too large and when new information can arrive at any time, dynamically. We propose a new incremental clustering algorithm based on closeness, an efficient and scalable approach which updates cluster and learn new information effectually. Confusion matrix is implemented to validate the results given by proposed system as compared to published results. The proposed systems achieves knowledge augmentation, incremental learning via incremental clustering without compromising quality of data and saving computing time and complexity.