A Survey of Classification Methods in Data Streams.
Abstract. With the advance in both hardware and software technologies, automated data generation and storage has become faster than ever. Such data is referred to as data streams. Streaming data is ubiquitous today and it is often a challenging task to store, analyze and visualize such rapid large volumes of data.Concept evolution in streaming data further magnifies the challenge of working with streaming data. Clustering is a data stream mining task which is very useful to gain insight of data and data characteristics. Clustering is also used as a pre-processing step in over all mining process for an example clustering is used for outlier detection and for building classification model.In this article, we present a survey of data stream clustering algorithms, providing a thorough discussion of the main design components of state-of-the-art algorithms. In addition, this work addresses the temporal aspects involved in data stream clustering, and presents an overview of the usually employed experimental methodologies.
Clustering is the subject of active research in several fields such as statistics, pattern recognition, and machine learning. This survey focuses on clustering in data mining. Data mining adds to clustering the complications of very large datasets with very many attributes of different types. This imposes unique.A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques KDD Bigdas, August 2017, Halifax, Canada of a class, based on the distribution of the words in the document.
STREAM Based on K-medians clustering Divide the whole data stream into chunks D, each with at most m data points. Pick a set of k points in each chunk, store it away as level-1 representatives. Repeat for each D. Once your representatives exceed your m, apply another level of clustering, using the stored weights in the process. Repeat this until done.
