Data stream clustering: A survey
Data stream mining is an active research area that has recently emerged to discover
knowledge from large amounts of continuously generated data. In this context, several data …
knowledge from large amounts of continuously generated data. In this context, several data …
Mining data streams: a review
The recent advances in hardware and software have enabled the capture of different
measurements of data in a wide range of fields. These measurements are generated …
measurements of data in a wide range of fields. These measurements are generated …
[كتاب][B] Data clustering: theory, algorithms, and applications
The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …
2007. Starting with the common ground and knowledge for data clustering, the monograph …
On evaluating stream learning algorithms
Most streaming decision models evolve continuously over time, run in resource-aware
environments, and detect and react to changes in the environment generating data. One …
environments, and detect and react to changes in the environment generating data. One …
BIRCH: an efficient data clustering method for very large databases
Finding useful patterns in large datasets has attracted considerable interest recently, and
one of the most widely studied problems in this area is the identification of clusters, or …
one of the most widely studied problems in this area is the identification of clusters, or …
Knowledge discovery from data streams
In the last two decades, machine learning research and practice has focused on batch
learning, usually with small datasets. Nowadays there are applications in which the data are …
learning, usually with small datasets. Nowadays there are applications in which the data are …
Issues in data stream management
Traditional databases store sets of relatively static records with no pre-defined notion of time,
unless timestamp attributes are explicitly added. While this model adequately represents …
unless timestamp attributes are explicitly added. While this model adequately represents …
Density-based clustering for real-time stream data
Existing data-stream clustering algorithms such as CluStream arebased on k-means. These
clustering algorithms are incompetent tofind clusters of arbitrary shapes and cannot handle …
clustering algorithms are incompetent tofind clusters of arbitrary shapes and cannot handle …
[PDF][PDF] Online outlier detection in sensor data using non-parametric models
Sensor networks have recently found many popular applications in a number of different
settings. Sensors at different locations can generate streaming data, which can be analyzed …
settings. Sensors at different locations can generate streaming data, which can be analyzed …
On density-based data streams clustering algorithms: A survey
Clustering data streams has drawn lots of attention in the last few years due to their ever-
growing presence. Data streams put additional challenges on clustering such as limited time …
growing presence. Data streams put additional challenges on clustering such as limited time …