نتایج جستجو برای: stream mining

تعداد نتایج: 143056  

2007
Georges Hébrail Yves Lechevallier

With the increase of computer use in all sectors of activity, more and more data are available as streams of structured records so that it is not possible to store all data before analyzing them in a data mining perspective. New data management systems have been studied to handle such data streams and new algorithms have been developed to perform stream mining. In this paper, we propose approac...

2007
Kazuhiro Shimizu Isamu Shioya Takao Miura

Frequent disjunctive pattern is known to be a sophisticated method of text mining in a single document that satisfies anti-monotonicity, by which we can discuss efficient algorithm based on APRIORI. In this work, we propose a new online and single-pass algorithm by which we can extract current frequent disjunctive patterns by a weighting method for past events from a news stream. And we discuss...

2006
Pascal Cheung-Mon-Chan Fabrice Clérot

Résumé. Au cours de ces dernières années, de nombreuses techniques de stream mining ont été proposées afin d’analyser des flux de données en temps réel. Dans cet article, nous montrons comment nous avons utilisé des techniques de stream mining permettant la recherche d’objets massifs hiérarchiques (hierarchical heavy hitters) dans un flux de données pour identifier en temps réel dans un réseau ...

2013
Neha Gupta Indrjeet Rajput

A data stream is a massive, continuous and rapid sequence of data elements. Mining data streams raises new problems for the data mining community about how to mine continuous high-speed data items that you can only have one look at. Due to this reason, traditional data mining approach is replaced by systems of some special characteristics, such as continuous arrival in multiple, rapid, time-var...

2003
Spiros Papadimitriou Anthony Brockwell Christos Faloutsos

Sensor devices and embedded processors are becoming ubiquitous, especially in measurement and monitoring applications. Automatic discovery of patterns and trends in the large volumes of such data is of paramount importance. The combination of relatively limited resources (CPU, memory and/or communication bandwidth and power) poses some interesting challenges. We need both powerful and concise “...

2006
Yang Cai Yong X. Hu

Mining physical properties from real-time sensor stream data is important to the atmospheric studies, ecology and oceanography. An FPGA-based reconfigurable sensory stream data mining processor is presented in this paper. The processor is based on Generalized Non-Linear Regression algorithm and trained with radiative transfer simulations and observations for autonomous detection of satellite me...

2016
Mahmood Hammoodi Frederic T. Stahl Mark Tennant

Data Streams are unbounded, sequential data instances that are generated very rapidly. The storage, querying and mining of such rapid flows of data is computationally very challenging. Data Stream Mining (DSM) is concerned with the mining of such data streams in real-time using techniques that require only one pass through the data. DSM techniques need to be adaptive to reflect changes of the p...

2015
Anita Chaudhari Neeta Patil Pratap Sakhare

A data stream is an ordered sequence of instances that can be read only once or a small number of times using limited computing and storage capabilities. Many application works on stream data like web search, network traffic, telephone call etc. In this application data is continuously changing based on time. In this paper we will discuss future trends of data mining that are used for analysis ...

2015
Kinnari Patel R G Mehta M M Raghuvanshi N N Vadnere S. McClean B. Scotney Michael R. Berthold Olga Troyanskaya Michael Cantor Gavin Sherlock Pat Brown Trevor Hastie Robert Tibshirani David Botstein Russ B. Altman Anjana Sharma Naina Mehta Iti Sharma Nadira Banu Kamal

Stream data mining is the process of excerpting knowledge structure from large, continuous data. For stream data, various techniques are proposed for preparing the data for data mining task. In recent years stream data have become a growing area for the researcher, but there are many issues occurring in classifying these data due to erroneous and noisy data. Change of trend in the data periodic...

Journal: :JCP 2009
Ling Chen Lingjun Zou Li Tu

A modified Fisher discriminate analysis method for classifying stream data is presented. To satisfy the realtime demand in classifying stream data, this method defines a new criterion for Fisher discriminate analysis. Since the new criterion requires less computation and memory space, it is much faster and more suitable for online processing in stream data environment. It can overcome the probl...

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