[PDF] Top 20 Clustering on demand for multiple data streams
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Clustering on demand for multiple data streams
... of clustering request reveals, recom- mendations for short-term or long-term investments are de- sired to be offered ...various clustering requirements at the same time?" Consequently, we devise in ... See full document
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Adaptive clustering for multiple evolving streams
... Adaptive Clustering for Multiple Evolving Streams Bi-Ru Dai, Jen-Wei Huang, Mi-Yen Yeh, and Ming-Syan Chen, Fellow, IEEE Abstract—In the data stream environment, the patterns generated ... See full document
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Clustering over Multiple Evolving Streams by Events and Correlations
... over Multiple Evolving Streams by Events and Correlations Mi-Yen Yeh, Bi-Ru Dai, and Ming-Syan Chen, Fellow, IEEE Abstract—In applications of multiple data streams such as stock market ... See full document
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An incremental algorithm for clustering spatial data streams: exploring temporal locality
... similar for a period. For example, ...based on the temporal locality feature, this study proposes an incremental clustering (IC) algorithm that clusters objects roughly by inducing ... See full document
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Constrained Clustering for the Evolving Data Stream
... the data min- ing systems, constraint-based mining has attracted a lot of research attention ...designed for static data sets, and are not investigated in the data stream ...Constrained ... See full document
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A Framework of Traveling Companion Discovery on Trajectory Data Streams
... stream. For example, couples would like to stay together on trips, military units operate in teams, families of birds, deer, and other animals often move together in species ... See full document
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Amazon Kinesis Data Streams
... process data from your Kinesis data ...support for languages other than Java is provided using a multi-language interface called the ...installed on your system because of the ...customize ... See full document
231
Mining spatio-temporal information on microblogging streams using a density-based online clustering method
... focused on only temporal ...acquired for free public Twitter ...model for event estimation, which is able to find the center and trajectory of event ...algorithms for mining Twitter text ... See full document
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Data Scheduling in Multiple Channel/Multiple Receiver Environments for Processing Data Requests with Timing Constraints
... the on-demand mode. Each data item in the database is classified as belonging to one of these two modes, denoted as a broadcast data set and an on-demand data set, respec- ... See full document
21
Mining Maximal Frequent Itemsets in Data Streams
... of data streams is shown in Figure 1. Therefore, data mining technologies have been studied for traditional datasets cannot be easily solved for the data stream ...require ... See full document
6
DSM-FI: An efficient algorithm for mining frequent itemsets in data streams
... streaming data makes it essential to use the online algorithms which require only one scan over the data streams for knowledge ...the data into the main memory or even in secondary ... See full document
19
An Efficient Algorithm for Mining Frequent Itemsets over the Entire History of Data Streams
... click streams in Web applications, performance measurement in network monitoring and traffic management, call records in telecommunications, ...streaming data differs from traditional data mining in ... See full document
10
DSM-FI: an efficient algorithm for mining frequent itemsets in data streams
... streaming data makes it essential to use the online algorithms which require only one scan over the data streams for knowledge ...the data into the main memory or even in secondary ... See full document
19
Stable Learned Bloom Filters for Data Streams
... Varying g and B. We also look into the joint effect of the number of groups g and the total storage budget B on the performance of g-SLBF. The results are shown in Figure 9b– 9d. We find that with a fixed storage ... See full document
13
An efficient clustering algorithm for market basket data based on small large ratios
... In each iteration of the refinement phase, algorithm SLR first com- putes the support values of items for identifying the large items and the small itenis in each cluster.. Then,[r] ... See full document
6
Chaotic particle swarm optimization for data clustering
... t Data clustering is a popular analysis tool for data statistics in several fields, including includes pattern recognition, data mining, machine learning, image analysis and ... See full document
4
Dynamic inventory rationing for systems with multiple demand classes and general demand processes
... strategy for the same inventory, the main assumption is that customers can be segmented according to their different service needs and ...customers. For motivation, this paper uses the example of a firm that ... See full document
8
Mining Frequent Itemsets for data streams over Weighted Sliding Windows
... windows for data stream mining. Based on the model, users can specify the number of windows, the size of a window, and the weight for each ...significant data section can be assigned a ... See full document
6
A new fuzzy clustering algorithms based on transformed data
... separable data transformation, the improved new algorithm, "Fuzzy Transformed C-Mean (FTCM)", is ...real data sets were applied to prove that the performance of the FTCM algorithm is better than the ... See full document
2
An Efficient Method for Mining Temporal Emerging Itemsets in Data Streams
... 4.2: Comparisons with Apriori In this experiment, we compare the average execution time in different support values between Apriori and EFI-Mine. Both of these two algorithms could find frequent itemsets. However, ... See full document
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