BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Requirements for clustering data streams
ACM SIGKDD Explorations Newsletter
Continuous queries over data streams
ACM SIGMOD Record
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A comparative analysis of artificial immune network models
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Using retrieval measures to assess similarity in mining dynamic web clickstreams
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Computer Networks: The International Journal of Computer and Telecommunications Networking - Web dynamics
Effective classification of noisy data streams with attribute-oriented dynamic classifier selection
Knowledge and Information Systems
Collaborative filtering in dynamic usage environments
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
A Dynamic Clustering Algorithm for Mobile Objects
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Incremental clustering of dynamic data streams using connectivity based representative points
Data & Knowledge Engineering
INDIE: An Artificial Immune Network for On-Line Density Estimation
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
A Scalable Framework For Segmenting Magnetic Resonance Images
Journal of Signal Processing Systems
A sliding window method for finding top-k path traversal patterns over streaming Web click-sequences
Expert Systems with Applications: An International Journal
On exploiting the power of time in data mining
ACM SIGKDD Explorations Newsletter
Expert Systems with Applications: An International Journal
COWES: Web user clustering based on evolutionary web sessions
Data & Knowledge Engineering
Associative classification with artificial immune system
IEEE Transactions on Evolutionary Computation
C-DenStream: Using Domain Knowledge on a Data Stream
DS '09 Proceedings of the 12th International Conference on Discovery Science
Outlier detection with streaming dyadic decomposition
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
Clustering data in an uncertain environment using an artificial immune system
Pattern Recognition Letters
A clustering algorithm for multiple data streams based on spectral component similarity
Information Sciences: an International Journal
SIC-means: a semi-fuzzy approach for clustering data streams using c-means
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
An incremental data stream clustering algorithm based on dense units detection
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
On clustering techniques for change diagnosis in data streams
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
Stream-dashboard: a framework for mining, tracking and validating clusters in a data stream
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
A single pass algorithm for clustering evolving data streams based on swarm intelligence
Data Mining and Knowledge Discovery
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Artificial Immune System (AIS) models hold many promises inthe field of unsupervised learning. However, existing models arenot scalable, which makes them of limited use in data mining. Wepropose a new AIS based clustering approach (TECNO-STREAMS)that addresses the weaknesses of current AIS models. Comparedto existing AIS based techniques, our approach exhibits superiorlearning abilities, while at the same time, requiring low memoryand computational costs. Like the natural immune system, thestrongest advantage of immune based learning compared to otherapproaches is expected to be its ease of adaptation to the dynamicenvironment that characterizes several applications, particularlyin mining data streams. We illustrate the ability of the proposedapproach in detecting clusters in noisy data sets, and in miningevolving user profiles from Web clickstream data in a single pass.TECNO-STREAMS adheres to all the requirements of clusteringdata streams: compactness of representation, fast incremental processingof new data points, and clear and fast identification of outliers.