A dynamic cluster maintenance system for information retrieval
SIGIR '87 Proceedings of the 10th annual international ACM SIGIR conference on Research and development in information retrieval
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Statistical grid-based clustering over data streams
ACM SIGMOD Record
Clustering on Demand for Multiple Data Streams
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
ACM SIGMOD Record
Online clustering of parallel data streams
Data & Knowledge Engineering
Sketching probabilistic data streams
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Unsupervised Clustering In Streaming Data
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
HClustream: A Novel Approach for Clustering Evolving Heterogeneous Data Stream
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A Grid and Density-Based Clustering Algorithm for Processing Data Stream
WGEC '08 Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing
Stream data clustering based on grid density and attraction
ACM Transactions on Knowledge Discovery from Data (TKDD)
Density-based clustering of data streams at multiple resolutions
ACM Transactions on Knowledge Discovery from Data (TKDD)
Incremental and Adaptive Clustering Stream Data over Sliding Window
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Connectivity based stream clustering using localised density exemplars
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Fast accurate fuzzy clustering through data reduction
IEEE Transactions on Fuzzy Systems
| Hi-index | 0.00 |
In recent years, data streaming has gained a significant importance. Advances in both hardware devices and software technologies enable many applications to generate continuous flows of data. This increases the need to develop algorithms that are able to efficiently process data streams. Additionaly, real-time requirements and evolving nature of data streams make stream mining problems, including clustering, challenging research problems. Fuzzy solutions are proposed in the literature for clustering data streams. In this work, we propose a Soft Incremental C-Means variant to enhance the fuzzy approach performance. The experimental evaluation has shown better performance for our approach in terms of Xie-Beni index compared with the pure fuzzy approach with changing different factors that affect the clustering results. In addition, we have conducted a study to analyze the sensitivity of clustering results to the allowed fuzziness level and the size of data history used. This study has shown that different datasets behave differently with changing these factors. Dataset behavior is correlated with the separation between clusters of the dataset.