Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 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
ACM Computing Surveys (CSUR)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Online novelty detection on temporal sequences
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A Principled Approach to Detecting Surprising Events in Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A self-organizing neural network for detecting novelties
Proceedings of the 2007 ACM symposium on Applied computing
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Proceedings of the 2008 ACM symposium on Applied computing
A comparison of extrinsic clustering evaluation metrics based on formal constraints
Information Retrieval
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
A Fast and Stable Incremental Clustering Algorithm
ITNG '10 Proceedings of the 2010 Seventh International Conference on Information Technology: New Generations
The Journal of Machine Learning Research
λ-Perceptron: An adaptive classifier for data streams
Pattern Recognition
Classification and novel class detection of data streams in a dynamic feature space
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
MOA: a real-time analytics open source framework
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
The ClusTree: indexing micro-clusters for anytime stream mining
Knowledge and Information Systems
Survey of clustering algorithms
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Severe constraints imposed by the nature of endless sequences of data collected from unstable phenomena have pushed the understanding and the development of automated analysis strategies, such as data clustering techniques. However, current clustering validation approaches are inadequate to data streams due to they do not properly evaluate representation of behavior changes. This paper proposes a novel function to continuously evaluate data stream clustering inspired in Lyapunov energy functions used by techniques such as the Hopfield artificial neural network and the Bidirectional Associative Memory (Bam). The proposed function considers three terms: i) the intra-cluster distance, which allows to evaluate cluster compactness; ii) the inter-cluster distance, which reflects cluster separability; and iii) entropy estimation of the clustering model, which permits the evaluation of the level of uncertainty in data streams. A first set of experiments illustrate the proposed function applied to scenarios of continuous evaluation of data stream clustering. Further experiments were conducted to compare this new function to well-established clustering indices and results confirm our proposal reflects the same information obtained with external clustering indices.