نتایج جستجو برای: stream mining
تعداد نتایج: 143056 فیلتر نتایج به سال:
An XML query stream is a massive and unbounded sequence of queries that are continuously generated at a fast speed from users over the Internet. Compared with traditional approaches of mining frequent user query patterns in static XML query databases, pattern mining in XML query streams is more challenging since several extra requirements need to be satisfied. In this paper, a mining algorithm ...
The real-time (or just-on-time) requirement associated with online association rule mining implies the need to expedite the analysis and validation of the many candidate rules, which are typically created from the discovered frequent patterns. Moreover, the mining process, from data cleaning to post-mining, can no longer be structured as a sequence of steps performed by the analyst, but must be...
The main idea behind this research relies on analytical data mining functions to handle data streams. Given the characteristics of the data stream, the new methods and techniques for stream data analysis must conduct advanced analysis and data mining over fast and large data streams to capture the trends, patterns and exceptions. Besides, much of such data resides at rather low level of abstrac...
Data stream can be conceived as a continuous and changing sequence of data that continuously arrive at a system to store or process. Examples of data streams include computer network traffic, phone conversations, web searches and sensor data etc. The data owners or publishers may not be willing to exactly reveal the true values of their data due to various reasons, most notably privacy consider...
Extended abstract 1- Introduction Gully erosion is one of the most important sources of sediment in the watersheds and a common phenomenon in semi-arid climate that affects vast areas with different morphological, soil and climatic conditions. This type of erosion is very dangerous due to the transfer of fertile soil horizons, and the reduction of water holding capacity also is a factor for s...
A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is likely to be changed as time goes by. However, most of mining algorithms or frequency approximation algorithms for a data stream do not able to extract the recent change of information in a data stream adaptively. This paper proposes a s...
Data stream mining has become a research area of some interest in recent years. The key challenge in data stream mining is extracting valuable knowledge in real time from a massive, continuous, dynamic data stream in only a single scan. Clustering is an efficient tool to overcome this problem. Data stream clustering can be applied in various fields such as financial transactions, telephone reco...
Data mining provides a way for finding hidden and useful knowledge from the large amount of data .usually we find any information by finding normal trends or distribution of data .But sometimes rare event or data object may provide information which is very interesting to us .Outlier detection is one of the task of data mining .It finds abnormal data point or sequence hidden in the dataset .Dat...
The data streams have recently emerged to address the problems of continuous data. Mining with data streams is the process of extracting knowledge structures from continuous, rapid data records [1]. An important goal in data stream mining is generation of compact representation of data. This helps in reducing time and space needed for further decision making process. In this paper we propose a ...
Frequent item mining, which deals with finding items that occur frequently in a given data stream over a period of time, is one of the heavily studied problems in data stream mining. A generalized version of frequent item mining is the persistent item mining, where a persistent item, unlike a frequent item, does not necessarily occur more frequently compared to other items over a short period o...
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