3,339 research outputs found
Change detection in categorical evolving data streams
Detecting change in evolving data streams is a central issue for accurate adaptive learning. In real world applications, data streams have categorical features, and changes induced in the data distribution of these categorical features have not been considered extensively so far. Previous work on change detection focused on detecting changes in the accuracy of the learners, but without considering changes in the data distribution.
To cope with these issues, we propose a new unsupervised change detection method, called CDCStream (Change Detection in Categorical Data Streams), well suited for categorical data streams. The proposed method is able to detect changes in a batch incremental scenario. It is based on the two following characteristics: (i) a summarization strategy is proposed to compress the actual batch by extracting a descriptive summary and (ii) a new segmentation algorithm is proposed to highlight changes and issue warnings for a data stream. To evaluate our proposal we employ it in a learning task over real world data and we compare its results with state of the art methods. We also report qualitative evaluation in order to show the behavior of CDCStream
Recommended from our members
A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation
open access articleThis article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses âmicroclustersâ and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStreamâs robustness to noise and resiliency to parameter changes
Network Sampling: From Static to Streaming Graphs
Network sampling is integral to the analysis of social, information, and
biological networks. Since many real-world networks are massive in size,
continuously evolving, and/or distributed in nature, the network structure is
often sampled in order to facilitate study. For these reasons, a more thorough
and complete understanding of network sampling is critical to support the field
of network science. In this paper, we outline a framework for the general
problem of network sampling, by highlighting the different objectives,
population and units of interest, and classes of network sampling methods. In
addition, we propose a spectrum of computational models for network sampling
methods, ranging from the traditionally studied model based on the assumption
of a static domain to a more challenging model that is appropriate for
streaming domains. We design a family of sampling methods based on the concept
of graph induction that generalize across the full spectrum of computational
models (from static to streaming) while efficiently preserving many of the
topological properties of the input graphs. Furthermore, we demonstrate how
traditional static sampling algorithms can be modified for graph streams for
each of the three main classes of sampling methods: node, edge, and
topology-based sampling. Our experimental results indicate that our proposed
family of sampling methods more accurately preserves the underlying properties
of the graph for both static and streaming graphs. Finally, we study the impact
of network sampling algorithms on the parameter estimation and performance
evaluation of relational classification algorithms
Dynamic Data Mining: Methodology and Algorithms
Supervised data stream mining has become an important and challenging data mining task in modern
organizations. The key challenges are threefold: (1) a possibly infinite number of streaming examples
and time-critical analysis constraints; (2) concept drift; and (3) skewed data distributions.
To address these three challenges, this thesis proposes the novel dynamic data mining (DDM)
methodology by effectively applying supervised ensemble models to data stream mining. DDM can be
loosely defined as categorization-organization-selection of supervised ensemble models. It is inspired
by the idea that although the underlying concepts in a data stream are time-varying, their distinctions
can be identified. Therefore, the models trained on the distinct concepts can be dynamically selected in
order to classify incoming examples of similar concepts.
First, following the general paradigm of DDM, we examine the different concept-drifting stream
mining scenarios and propose corresponding effective and efficient data mining algorithms.
⢠To address concept drift caused merely by changes of variable distributions, which we term
pseudo concept drift, base models built on categorized streaming data are organized and
selected in line with their corresponding variable distribution characteristics.
⢠To address concept drift caused by changes of variable and class joint distributions, which we
term true concept drift, an effective data categorization scheme is introduced. A group of
working models is dynamically organized and selected for reacting to the drifting concept.
Secondly, we introduce an integration stream mining framework, enabling the paradigm advocated by
DDM to be widely applicable for other stream mining problems. Therefore, we are able to introduce
easily six effective algorithms for mining data streams with skewed class distributions.
In addition, we also introduce a new ensemble model approach for batch learning, following the same
methodology. Both theoretical and empirical studies demonstrate its effectiveness.
Future work would be targeted at improving the effectiveness and efficiency of the proposed
algorithms. Meantime, we would explore the possibilities of using the integration framework to solve
other open stream mining research problems
- âŚ