Results 271 to 280 of about 24,371,806 (309)
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2015
Advances in hardware technology have led to new ways of collecting data at a more rapid rate than before. For example, many transactions of everyday life, such as using a credit card or a phone, lead to automated data collection. Similarly, new ways of collecting data, such as wearable sensors and mobile devices, have added to the deluge of dynamically
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Advances in hardware technology have led to new ways of collecting data at a more rapid rate than before. For example, many transactions of everyday life, such as using a credit card or a phone, lead to automated data collection. Similarly, new ways of collecting data, such as wearable sensors and mobile devices, have added to the deluge of dynamically
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Change Itemset Mining in Data Streams
2013Data stream mining is becoming very important in many application areas, such as the stock market. A data stream consists of an ordered sequence of instances and because there are usually a large number of instances along with limited computing and storage capabilities, algorithms that read the data only once are preferred. There has been some research
Minmin Zhang +2 more
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Data Stream Mining: the Bounded Rationality.
Informatica (Slovenia), 2013The developments of information and communication technologies dramatically change the data collection and processing methods. Data mining is now moving to the era of bounded rationality. In this work we discuss the implications of the resource constraints impose by the data stream computational model in the design of learning algorithms.
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2019
In recent years, advancement in technologies has made it possible for most of the present-day organizations to store and record large streams of data. Such data sets which continuously and rapidly grow over time are referred to as data streams. Mining of such data streams is a unique opportunity and also a challenging task.
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In recent years, advancement in technologies has made it possible for most of the present-day organizations to store and record large streams of data. Such data sets which continuously and rapidly grow over time are referred to as data streams. Mining of such data streams is a unique opportunity and also a challenging task.
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Clustering-training for Data Stream Mining
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006Mining data streams has attracted much attention recently. Labeled samples needed by most current stream classification methods are more difficult and expensive to obtain than unlabeled ones. This paper proposed a semi-supervised learning algorithm - clustering-training to utilize the unlabeled samples.
Shuang Wu +2 more
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Data Discretization for Data Stream Mining
2023Anis Cherfi, Kaouther Nouira
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Mining Sequential Patterns in Data Stream
2009We present a new algorithm of mining sequential patterns in data stream. In recent years data stream emerges as a new data type in many applications. When processing data stream, the memory is fixed, new stream elements flow continuously. The stream data can not be paused or completely stored.
Qinhua Huang, Weimin Ouyang
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Stream mining on univariate uncertain data
Applied Intelligence, 2013In this paper, we propose mining frequent patterns from univariate uncertain data streams, which have a quantitative interval for each attribute in a transaction and a probability density function indicating the possibilities that the values in the interval appear.
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Data Stream Mining: Challenges and Techniques
2010 22nd IEEE International Conference on Tools with Artificial Intelligence, 2010Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Their sheer volume and speed pose a great challenge for the data mining community to mine them. Data streams demonstrate several unique properties: infinite length, concept-drift, concept-evolution, and feature ...
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Mining Regular Patterns in Data Streams
2010Discovering interesting patterns from high-speed data streams is a challenging problem in data mining. Recently, the support metric-based frequent pattern mining from data stream has achieved a great attention. However, the occurrence frequency of a pattern may not be an appropriate criterion for discovering meaningful patterns.
Syed Khairuzzaman Tanbeer +2 more
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