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About 10,000 papers for “Data stream mining”

Data Stream Mining

Gaber, Mohamed Medhat, Zaslavsky, Arkady, Krishnaswamy, Shonali · Own your potential (DEAKIN) · 2010

Cited by 0

Stream data mining

Rutkowski, Leszek, Jaworski, Maciej, Duda, Piotr · CERN Document Server (European Organization for Nuclear Research) · 2019

Cited by 0

Data stream mining

Wan, Li · DR-NTU (Nanyang Technological University) · 2009

The data stream mining problem has been studied extensively in recent years, due to the greatease in collection of stream data. The essential to a data stream mining algorithms is that we can only read data once. Unfortunately, most of traditional data mining algorithms do not have such single-scan property. Usually, data stream is considered as semi-in¯nite. It is impossible
\nto store all the past data with limited resources. Thus, mining high dimensional data streams
\nis a challenging task. In this report, we are going to propose some interesting observations on
\nfeature quality stream(FQS), which is obtained from data stream in real time, and a frame-
\nwork to analyze such stream. The analysis results of FQS are used to reduce the dimension of
\ndata streams. We will also propose a data stream mining framework called MR-Stream. It is
\na e±cient data stream clustering framework with the following properties: (1) computes and
\nupdates synopsis information in constant time; (2) allows users to discover clusters at multiple
\nresolutions; (3) determines the right time for users to generate clusters from the synopsis in-
\nformation; (4) generates clusters of higher purity than existing algorithms; and (5) determines
\nthe right threshold function for density-based clustering based on the fading model of stream
\ndata. MR-Stream can be extend to solve classi¯cation problem. The classi¯cation results ob-
\ntained from the online component of MR-Stream framework are in realtime. The result given
\nby MR-Stream is presented as a probability distribution table over di®erent classes.

Cited by 0

Data stream mining

Huang, Lelun. · DR-NTU (Nanyang Technological University) · 2010

Data streaming is one area of data mining that has been studied extensively. One problem of data streaming is to detect noise and random shapes when clustering, where basic K-Means usually fail. Some researchers suggested density based clustering according to a decay function; one typical example is D-Stream. However, its universal decay factor and cluster on a fixed interval do not achieve optimal efficiency regarding to space and time complexity. In this report, we made an attempt to improve both space and time complexity of D-Stream. Our integrated work DCC-Stream follows conventional online-offline approach in stream mining. We describe our algorithm as two parts: online and offline parts. Online part accumulates historical data as synopsis information and makes use of two sentinels to detect whether offline parts should be invoked. Offline part contains two separate parts, one is responsible for updating density, the other is for clustering. The experimental evaluation shows that our algorithm achieves both significant improvements on time and space complexity. The results show time usage is greatly reduced while maintain similar purity. In addition, the algorithm also achieves better space usage.

Cited by 0

Data Stream Mining

Mohamed Medhat Gaber, Arkady Zaslavsky, Shonali Krishnaswamy · 2009

Cited by 56

A. Data Mining And Stream Data Mining

Mistry Vinay R, Ms. Astha Baxi · 2015

Abstract- The rapid development in the e-commerce and distributed computing generates millions of the transaction, continuously. This continues arrival of data is considered as a DataStream. Data mining process for classification needs considerable modification to cope with continuous data. As Mining continues stream of data, conceptually has infinite length, and the class of data may change in sudden or gradual or hike, for which classification model is completely unknown or not prepared. Here investigation is made on different techniques proposed for the data stream classification using decision trees. Different approaches of decision tree classification for the stream data are analyzed & compared. The primary comparison parameters are time and accuracy. Also shown efforts made for handling the change in the concept and they are compared in terms of memory, technique and accuracy.

Cited by 0

stream: Infrastructure for Data Stream Mining

Michael Hahsler · 2013

A framework for data stream modeling and associated data mining tasks such as clustering and classification. The development of this package was supported in part by NSF IIS-0948893, NSF CMMI 1728612, and NIH R21HG005912. Hahsler et al (2017) &lt;<a href="https://doi.org/10.18637%2Fjss.v076.i14" target="_top">doi:10.18637/jss.v076.i14</a>&gt;.

Cited by 10Open access

Data Mining in Data Stream

Sýkora, Petr · Brno University of Technology Digital Library (Brno University of Technology) · 2009

Tato práce pojednává o dolování v proudu dat, což představuje v současné době velice rychle se rozvíjející oblast informačních technologií. Jsou vysvětleny obecné principy dolování v datech, pojem proud dat, a jsou popsány metody jeho předzpracování a následně algoritmy pro dolování v proudu dat. Podrobně jsou rozebrány algoritmy VFDT a CVFDT pro klasifikaci. Dále je pozornost věnována časoprostorovým datům a možnostem jejich dolování. V rámci praktické části práce byla navrhnuta a implementována aplikace pro klasifikaci a predikci časoprostorových událostí (dopravních zácep) v proudu dat ze silničního provozu. Pro klasifikaci byly použity algoritmy VFDT a CVFDT. Program byl otestován na datech generovaných simulačním nástrojem SUMO.

Cited by 0

Concurrent data stream mining

Wang, Wenwen. · DR-NTU (Nanyang Technological University) · 2009

Given the characteristics of streaming data---read-once only and infinitely&#13;\nstreaming, it is desirable to perform multiple, concurrent types of mining on streaming data to the fullest extent permitted by resource constraints. However, to the best of our knowledge, conventional stream mining algorithms focused on single, standalone mining. In this report, we made an attempt to achieve concurrent classification and clustering on streaming data. Our integrated framework---the MM-Stream---follows conventional online-offline approaches in stream mining. We describe our framework in general by dividing it into two components, online component and offline component: as data stream in, the online component completes all necessary process within constant time and drops data; as a mining request is issued by the user(s), the offline component performs the mining task(s) from the information collected by online component. We implemented and evaluated the algorithm. The performance evaluation showed that the performance of MM-Stream is comparable or better than existing standalone stream mining algorithms (D-Stream, On-Demand-Stream classifier). We investigated how the performance of such integrated mining compares with the purity and accuracy of standalone clustering and classification respectively. The results showed that, with concurrent mining, we can receive almost-double throughputs, without any degrade on the quality on mining results. We believe that our successful experimentation in MM-Stream's concurrent stream classification and clustering paves the way for incorporating more concurrent data mining tasks to maximize the outputs of stream data mining.

Cited by 0

A Data Stream Mining System

Hetal Thakkar, Barzan Mozafari, Carlo Zaniolo · 2008

On-line data stream mining has attracted much research interest, but systems that can be used as a workbench for online mining have not been researched, since they pose many difficult research challenges. The proposed system addresses these challenges by an architecture based on three main technical advances, (i) introduction of new constructs and synoptic data structures whereby complex KDD queries can be easily expressed and efficiently supported, (ii) an integrated library of mining algorithms that are fast & light enough to be effective on data streams, and (iii) support for Mining Model Definition Language (MMDL) that allows users to define new mining algorithms as a set of tasks and flows. Thus, the proposed system provides an extensible workbench for online mining, which is beyond the existing proposals for even static mining.

Cited by 7

Data stream management and mining

Georges Hébrail · HAL (Le Centre pour la Communication Scientifique Directe) · 2014

Abstract. This paper provides an introduction to the field of data stream management and mining. The increase of data production in operational information systems prevents from monitoring these systems with the old paradigm of storing data before analyzing it. New approaches have been developed recently to process data ‘on the fly ’ considering they are produced in the form of structured data streams. These approaches cover both querying and mining data.

Cited by 10Open access