scholarly journals Data Mining Models of High Dimensional Data Streams, and Contemporary Concept Drift Detection Methods: a Comprehensive Review

2018 ◽  
Vol 7 (3.6) ◽  
pp. 148
Author(s):  
M Sankara Prasanna Kumar ◽  
A P. Siva Kumar ◽  
K Prasanna
Keyword(s):  
Data Streams ◽  
Concept Drift ◽  
High Dimensional Data ◽  
Detection Methods ◽  
High Dimensional ◽  
Distributed Data ◽  
Stable Period ◽  
Multiple Data ◽  
Multiple Data Streams ◽  
Concept Drifts

Concept drift is defined as the distributed data across multiple data streams that change over the time. Concept drift is visible only when the type of collected data changes after some stable period. The emergence of concept drift in data streams leads to increase misclassification and performing degradation of data streams. In order to obtain accurate results, identification of such concept drifts must be visible. This paper focused on a review of the issues related to identifying the changes occurred in the various multivariate high dimensional data streams. The insight of the manuscript is probing the inbuilt difficulties of existing contemporary change-detection methods when they encounter during data dimensions scales.  

Incomplete high dimensional data streams clustering

2020 ◽  
Vol 39 (3) ◽  
pp. 4227-4243
Author(s):  
Fatma M. Najib ◽  
Rasha M. Ismail ◽  
Nagwa L. Badr ◽  
Tarek F. Gharib
Keyword(s):  
Data Streams ◽  
Missing Values ◽  
High Dimensional Data ◽  
Subspace Clustering ◽  
Streaming Data ◽  
High Dimensionality ◽  
High Dimensional ◽  
Data Sets ◽  
Multiple Data ◽  
Sensitivity Specificity

Many recent applications such as sensor networks generate continuous and time varying data streams that are often gathered from multiple data sources with some incompleteness and high dimensionality. Clustering such incomplete high dimensional streaming data faces four constraints which are 1) data incompleteness, 2) high dimensionality of data, 3) data distribution, 4) data streams’ continuous nature. Thus, in this paper, we propose the Subspace clustering for Incomplete High dimensional Data streams (SIHD) framework that overcomes the above clustering issues. The proposed SIHD provides continuous missing values imputation for incomplete streams based on the corresponding nearest-neighbors’ intervals. An adaptive subspace clustering mechanism is proposed to deal with such incomplete high dimensional data streams. Our experimental results using two different data sets prove the efficiency of the proposed SIHD framework in clustering such incomplete high dimensional data streams in terms of accuracy, precision, sensitivity, specificity, and F-score compared to five algorithms GFCM, GBDC-P2P, DS, Ensemble, and DMSC. The proposed SIHD improved: 1) the accuracy on average over the five algorithms in the same mentioned order by 11.3%, 10.8%, 6.5%, 4.1%, and 3.6%, 2) the precision by 15%, 10.6%, 6.4%, 4%, and 3.5%, 3) the sensitivity by 16.6%, 10.6%, 5.8%, 4.2%, and 3.6%, 4) the specificity by 16.8%, 10.9%, 6.5%, 4%, and 3.5%, 5) the F-score by 16.6%, 10.7%, 6.6%, 4.1%, and 3.6%.

scholarly journals Actionable intelligence and online learning for semantic computing

2017 ◽  
Vol 01 (01) ◽  
pp. 1630011
Author(s):  
Cem Tekin ◽  
Mihaela van der Schaar
Keyword(s):  
Online Learning ◽  
Data Streams ◽  
Concept Drift ◽  
Relevant Information ◽  
High Dimensional ◽  
Time Varying ◽  
Stream Mining ◽  
Multiple Data ◽  
New Challenges ◽  
Semantic Computing

As the world becomes more connected and instrumented, high dimensional, heterogeneous and time-varying data streams are collected and need to be analyzed on the fly to extract the actionable intelligence from the data streams and make timely decisions based on this knowledge. This requires that appropriate classifiers are invoked to process the incoming streams and find the relevant knowledge. Thus, a key challenge becomes choosing online, at run-time, which classifier should be deployed to make the best possible predictions on the incoming streams. In this paper, we survey a class of methods capable to perform online learning in stream-based semantic computing tasks: multi-armed bandits (MABs). Adopting MABs for stream mining poses, numerous new challenges requires many new innovations. Most importantly, the MABs will need to explicitly consider and track online the time-varying characteristics of the data streams and to learn fast what is the relevant information out of the vast, heterogeneous and possibly highly dimensional data streams. In this paper, we discuss contextual MAB methods, which use similarities in context (meta-data) information to make decisions, and discuss their advantages when applied to stream mining for semantic computing. These methods can be adapted to discover in real-time the relevant contexts guiding the stream mining decisions, and tract the best classifier in presence of concept drift. Moreover, we also discuss how stream mining of multiple data sources can be performed by deploying cooperative MAB solutions and ensemble learning. We conclude the paper by discussing the numerous other advantages of MABs that will benefit semantic computing applications.

scholarly journals Comparing multiple data streams to assess free-floating carsharing use

2018 ◽  
Vol 32 ◽  
pp. 617-626 ◽  
Author(s):  
Grzegorz Wielinski ◽  
Martin Trépanier ◽  
Catherine Morency ◽  
Khandker Nurul Habib
Keyword(s):  
Data Streams ◽  
Multiple Data ◽  
Multiple Data Streams

Clustering Algorithm for Multiple Data Streams Based on Data Cloud Node

2013 ◽  
Vol 462-463 ◽  
pp. 247-250
Author(s):  
Sa Li ◽  
Liang Shan Shao
Keyword(s):  
Data Streams ◽  
Minimum Distance ◽  
Clustering Algorithm ◽  
Cloud Model ◽  
Data Sequence ◽  
Multiple Data ◽  
Multiple Data Streams ◽  
Model Algorithm

Multiple data streams clustering aims to clustering multiple data streams according to their similarity while tracking their changes with time . This paper proposes M_SCCStream algorithm based on cloud model. Algorithm introduces data cloud node structure with hierarchical characteristics to represent different granularity data sequence and takes the entropy indicated the degree of data changes. Algorithm finds micro_clustering with the minimum distance and then obtains the clustering result of multiple data streams through calculating the correlation degrees of micro_clustering. The experiment proves that the algorithm has higher quality and stability.

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