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Fast Rule Mining Over Multi-Dimensional Windows [chapter]

Mahashweta Das, Deepak P, Prasad M Deshpande, Ramakrishnan Kannan
2011 Proceedings of the 2011 SIAM International Conference on Data Mining  
Such interactive rule mining over multi-dimensional query windows is difficult since rule mining is computationally expensive.  ...  The data in a warehouse being multi-dimensional, it is often useful to mine rules over subsets of data defined by selections over the dimensions.  ...  Multi-Dimensional Windows A multi-dimensional query window is a combination of one-dimensional windows over a set of attributes, having one window per chosen attribute.  ... 
doi:10.1137/1.9781611972818.50 dblp:conf/sdm/DasPDK11 fatcat:37a2lo57dfdl7dgj6vrpjl7hk4

TCMiner: A High Performance Data Mining System for Multi-dimensional Data Analysis of Traditional Chinese Medicine Prescriptions [chapter]

Chuan Li, Changjie Tang, Jing Peng, Jianjun Hu, Lingming Zeng, Xiaoxiong Yin, Yongguang Jiang, Juan Liu
2004 Lecture Notes in Computer Science  
This paper introduces the architecture and algorithms of TCMiner: a high performance data mining system for multi-dimensional data analysis of Traditional Chinese Medicine prescriptions.  ...  The system has the following competing advantages: (1) High Performance (2) Multi-dimensional Data Analysis Capability (3) High Flexibility (4) Powerful Interoperability (5) Special Optimization for TCM  ...  Major Components The Multi-Dimensional Data Analysis Engine is responsible for most multi-dimensional data analysis tasks.  ... 
doi:10.1007/978-3-540-30466-1_23 fatcat:44adxymdyvgexjyjszdnoxr2zq

Knowledge Discovery from Static Datasets to Evolving Data Streams and Challenges

V. SiddaReddy, M. Narendra, K. Helini
2014 International Journal of Computer Applications  
In this research work, we will survey the main techniques and applications of data mining and data stream mining.  ...  Traditional data mining techniques can not be easily applied to the data stream mining due to unique characteristics of data streams.  ...  Where as stream mining queries are often complex need multi-level and multi-dimensional processing so the mining such data streams are most challenging task.  Data stream elements change rapidly overtime  ... 
doi:10.5120/15284-3915 fatcat:ajhugnqnw5bzviqzhkjyfz4m4a

Mining Multi-dimensional Quantitative Associations [chapter]

Michał Okoniewski, Łukasz Gancarz, Piotr Gawrysiak
2003 Lecture Notes in Computer Science  
The new form of quantitative and multi -dimensional association rules, unlike other approaches, does not require the discretization of real value attributes as a preprocessing step.  ...  An example of such algorithm is Window algorithm proposed  ...  The problem of fast s election of tuples within a rectangle that inuences the complexity of the algorithm may be solved by the latest advances in the area of multi -dimensional indexing.  ... 
doi:10.1007/3-540-36524-9_22 fatcat:xhehjy75r5fwpjci6hx4rr54by

Visual transformation for interactive spatiotemporal data mining

Yang Cai, Richard Stumpf, Timothy Wynne, Michelle Tomlinson, Daniel Sai Ho Chung, Xavier Boutonnier, Matthias Ihmig, Rafael Franco, Nathaniel Bauernfeind
2007 Knowledge and Information Systems  
The purpose of this study is to bridge the gap with transformation algorithms for mapping the data from an abstract space to an intuitive one, which includes shape correlation, periodicity, multi-physics  ...  We assign an interactive window for two-dimensional data points. In Multi-Physics Transform Conventional data mining models do not involve physical parameters.  ...  Figure 2 shows an illustration of the system architecture where the cellular automata model consists of Bayesian inference, artificial life rules, and multi-physics rules such as multi-body collision,  ... 
doi:10.1007/s10115-007-0075-5 fatcat:oj6s6jlcbnb6ljqi3pxyrbckwm

Real-Time Knowledge Discovery and Dissemination for Intelligence Analysis

Bhavani M. Thuraisingham, Latifur Khan, Murat Kantarcioglu, Sonia Chib, Jiawei Han, Sang Hyuk Son
2009 2009 42nd Hawaii International Conference on System Sciences  
This paper describes the issues and challenges for real-time knowledge discovery and then discusses approaches and challenges for real-time data mining and stream mining.  ...  All these applications demand for the development of mechanisms for multi-dimensional analysis and mining of changes, trends, and unusual patterns of data streams, with low cost and fast response time.  ...  are interested in finding characteristics, rules, unusual patterns, and dynamic changes (such as trends and outliers) at relatively high levels of abstraction and in multi-dimensional space.  ... 
doi:10.1109/hicss.2009.363 dblp:conf/hicss/ThuraisinghamKKCHS09 fatcat:euta372kxnfwngtpaf6gthsub4

A Survey Paper on Data Stream Mining

Shazia Nousheen M, Dr Prasad G. R
2016 International Journal of Engineering Research and  
Data stream mining is the way of extracting meaningful knowledge from this huge volumes of information.  ...  The aim of this survey is to provide a brief view on different classification techniques in data mining.  ...  To adjust this in information streams, VFDT algorithm was further formed into the Concept-adapting Very Fast Decision Tree calculation (CVFDT) it runs VFDT over sliding windows, which are fixed, keeping  ... 
doi:10.17577/ijertv5is080107 fatcat:rdklg2mu5vb4tme2dkjjb3ujd4

Incremental classification using Feature Tree [article]

Nishant Vadnere, R.G.Mehta, D.P.Rana, N.J.Mistry, M.M.Raghuwanshi
2014 arXiv   pre-print
Some of the issues occurred in classifying stream data that have significant impact in algorithm development are size of database, online streaming, high dimensionality and concept drift.  ...  The concept drift occurs when the properties of the historical data and target variable change over time abruptly in such a case that the predictions will become inaccurate as time passes.  ...  [41] proposed CVFDT (Concept-adapting Very Fast Decision Trees learner) which can work with concept drift using the sliding window concept on incoming data.  ... 
arXiv:1402.1257v2 fatcat:qni7g33mjbbpzhzflw7nxlb4l4

AN INNOVATIVE IDEA TO DISCOVER THE TREND ON MULTI-DIMENSIONAL SPATIO-TEMPORAL DATASETS

N. Naga Saranya .
2014 International Journal of Research in Engineering and Technology  
So this research offers an innovative idea to discover the trend on multi-dimensional spatio-temporal datasets. Here it briefly describes the scope and relevancy of spatiotemporal data.  ...  This research offers an innovative idea to discover the trend on multi-dimensional spatio-temporal datasets. Here it briefly describes the scope and relevancy of spatio-temporal data.  ...  The objective of this research is to develop a novel algorithm for trend discovery on multi dimensional Spatio-temporal databases.  ... 
doi:10.15623/ijret.2014.0303046 fatcat:qoyuzflmbvfozgle4nmwkvl23q

Research Progress of Stream Data Query in Network Space

Yi Wu, Jianjun Zhou
2015 International Journal of Database Theory and Application  
Aiming at collateral problems in multi-core processing architecture, Park [39] reduced comparison of dominance relationship between non-Skyline-query points by the order recombination of the access sequence  ...  Like onedimensional Harr wave, multi-dimensional Harr wave gets original multi-dimensional matrix through coefficient matrix recovery.  ...  Reference [20] propose a compound sliding window model to compute the distinct values over basic sliding windows in an incremental way.  ... 
doi:10.14257/ijdta.2015.8.5.14 fatcat:2jfxpbvlgfctxe7rl24gd2hxom

Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams

Jiawei Han, Yixin Chen, Guozhu Dong, Jian Pei, Benjamin W. Wah, Jianyong Wang, Y. Dora Cai
2005 Distributed and parallel databases  
For fast online multi-dimensional analysis of stream data, three important techniques are proposed for efficient and effective computation of stream cubes.  ...  In this paper, we propose an architecture, called stream cube, to facilitate on-line, multi-dimensional, multi-level analysis of stream data.  ...  Furthermore, we believe that a very important direction is to further develop data mining methods to take advantage of multi-dimensional, multi-level stream cubes for single-scan on-line mining to discover  ... 
doi:10.1007/s10619-005-3296-1 fatcat:4voq77s7bresderdwky3ivmamq

Research issues in data stream association rule mining

Nan Jiang, Le Gruenwald
2006 SIGMOD record  
This raises new issues that need to be considered when developing association rule mining techniques for stream data.  ...  There exist emerging applications of data streams that require association rule mining, such as network traffic monitoring and web click streams analysis.  ...  Multidimensional Stream Data In applications where stream data are multi-dimensional in nature, multi-dimensional processing techniques for association rule mining need to be considered.  ... 
doi:10.1145/1121995.1121998 fatcat:2l22o3uioja4nfy5iagmnqiduu

Parallel Algorithms for Mining Association Rules in Time Series Data [chapter]

Biplab Kumer Sarker, Takaki Mori, Toshiya Hirata, Kuniai Uehara
2003 Lecture Notes in Computer Science  
However, these data sets with high dimensionality are enormous in size results in possibly large number of mined dependencies. This strongly motivates the need of efficient parallel algorithms.  ...  For example, association rule discovered from motion data about walking is "when right hand is up then the left hand and left knee are down".  ...  Also their high dimensionality results in possibly large number of mined association rules. This strongly motivates the need of efficient parallel algorithms.  ... 
doi:10.1007/3-540-37619-4_28 fatcat:lgafuefnh5d23jyf4nro4ievgu

Noval Stream Data Mining Framework under the Background of Big Data

Wenquan Yi, Fei Teng, Jianfeng Xu
2016 Cybernetics and Information Technologies  
However, traditional steam data mining methods are not effective enough for handling high dimensional data set because these methods are not fit for the characteristics of stream data.  ...  Research on stream data mining mainly focuses on frequent item sets mining, clustering and classification.  ...  In the same year, multi-thread mining is mentioned in reference [10] .  ... 
doi:10.1515/cait-2016-0053 fatcat:fpqjhuan7jg23gjm2ivntaqvzq

Design and analysis of a multi-dimensional data sampling service for large scale data analysis applications

Xi Zhang, T. Kurc, J. Saltz, S. Parthasarathy
2006 Proceedings 20th IEEE International Parallel & Distributed Processing Symposium  
In this paper we present a scalable sampling implementation that supports efficient, multi-dimensional spatio-temporal sample generation on dynamic, large scale datasets stored on a storage cluster.  ...  Sampling is a widely used technique to increase efficiency in database and data mining applications operating on large dataset.  ...  This index structure facilitates fast retrieval of multi-dimensional samples that encompass constraints over time and space.  ... 
doi:10.1109/ipdps.2006.1639315 dblp:conf/ipps/ZhangKSP06 fatcat:emeykbxpijdtxlqbwdfsid36s4
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