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Efficient mining of correlated sequential patterns based on null hypothesis

Cindy Xide Lin, Ming Ji, Marina Danilevsky, Jiawei Han
2012 Proceedings of the 2012 international workshop on Web-scale knowledge representation, retrieval and reasoning - Web-KR '12  
We then propose PSBSpan, an efficient mining algorithm based on the framework of the pattern-growth methodology which mines frequent correlated sequential patterns.  ...  In this paper, we formally propose and define the task of mining frequent correlated sequential patterns from a sequential database.  ...  In this paper, we propose a novel algorithm for mining frequent correlated sequential patterns, based on extensions of the pattern growth methodology [23] .  ... 
doi:10.1145/2389656.2389660 dblp:conf/cikm/LinJDH12 fatcat:sdycbkdfdfebtgqhbfuwwjahbq

Efficient Discovery of Large Synchronous Events in Neural Spike Streams [article]

Raajay Viswanathan and P. S. Sastry and K.P. Unnikrishnan
2010 arXiv   pre-print
Recently, techniques have been developed to efficiently count the frequency of synchronous firing patterns.  ...  We focus on the synchronous firings of groups of neurons as these have been shown to play a major role in coding and communication.  ...  However, these algorithms are all essentially correlation based and also count the frequency of all possible patterns.  ... 
arXiv:1006.1543v1 fatcat:g5ov2jn2fvf4fkerqe3cbquqza

Statistical significance of sequential firing patterns in multi-neuronal spike trains

Casey O. Diekman, P.S. Sastry, K.P. Unnikrishnan
2009 Journal of Neuroscience Methods  
The structure of our null hypothesis also allows us to rank order the detected patterns. We demonstrate our method on simulated spike trains.  ...  We specify the null hypothesis in terms of a bound on the conditional probabilities that characterize the influence of one neuron on another.  ...  Most of KPU's work was performed when he was at the GM R&D Center, Warren, MI. Appendix A.  ... 
doi:10.1016/j.jneumeth.2009.06.018 pmid:19559053 fatcat:ovrfgo37avftfggjaslgvj3cia

Special issue on discovery science

Nathalie Japkowicz, Stan Matwin
2017 Machine Learning  
In the article entitled "Memory-Adaptive High Utility Sequential Pattern Mining over Data Streams" by Morteza Zihayat, Yan Chen and Aijun An, the authors look at the problem of high utility sequential  ...  In the article entitled "Confidence Curves: An Alternative to Null Hypothesis Significance Testing for the Comparison of Classifiers" by Daniel Berrar, the author contends that Null Hypothesis Significance  ... 
doi:10.1007/s10994-016-5625-1 fatcat:26t6nenhbrcrlczvy3j2ho2kby

Frequent Pattern Mining and Current State of the Art

Kalli Srinivasa Nageswara Prasad, S. Ramakrishna
2011 International Journal of Computer Applications  
General Terms Data Mining, Market Basket Analysis, Itemset.  ...  Identifying the association rules in large databases play a key role in data mining.  ...  The research varies from efficient and scalable algorithms to most research frontiers; including sequential, structured, correlative mining, associative classification and frequent pattern based clustering  ... 
doi:10.5120/3114-4279 fatcat:nza37yy2prft3jrueownimthw4

A Parameter-Free Approach for Mining Robust Sequential Classification Rules

Elias Egho, Dominique Gay, Marc Boulle, Nicolas Voisine, Fabrice Clerot
2015 2015 IEEE International Conference on Data Mining  
We also develop a parameter-free algorithm to efficiently mine sequential patterns from the model space.  ...  inductive performance than the state-of-the-art sequential pattern based classifiers.  ...  MINING SEQUENTIAL CLASSIFICATION RULES Mining sequential patterns [20] is a NP-hard problem.  ... 
doi:10.1109/icdm.2015.87 dblp:conf/icdm/EghoGBVC15 fatcat:3fwfgknuwrfivgom7thxxohfey

Selective Inference Approach for Statistically Sound Predictive Pattern Mining [article]

Shinya Suzumura, Kazuya Nakagawa, Mahito Sugiyama, Koji Tsuda, Ichiro Takeuchi
2016 arXiv   pre-print
In selective inference, statistical inferences (such as statistical hypothesis testing) are conducted based on sampling distributions conditional on a selection event.  ...  However, in pattern mining problems, it is difficult to characterize the entire selection process of mining algorithms.  ...  For concreteness, we consider pattern mining algorithms for discovering the top k patterns based on the statistic {s j } j∈[J] .  ... 
arXiv:1602.04601v2 fatcat:avnxctrocfdd5pdccqhf3ym5wu

FREQUENT CORRELATED PERIODIC PATTERN MINING FOR LARGE VOLUME SET USING TIME SERIES DATA

G. M. Karthik, S. Karthik
2014 Journal of Computer Science  
The analysis of time correlation measure tend to improvise the performance based on real time data sets and the result proves the algorithm efficiency by shifting the data sets to various domain towards  ...  Frequent pattern mining has been a widely used in the area of discovering association and correlations among real data sets.  ...  We select measures appropriate to our mining task • To demonstrate the outstanding performance of our algorithm based on correlated relationship in terms of both efficiency and effectiveness on datasets  ... 
doi:10.3844/jcssp.2014.2105.2116 fatcat:boyh7n3isfgz5bzw3flirgazwu

Some Further Evidence on the Behaviour of Stock Returns in India

Gourishankar S Hiremath, Bandi Kamaiah
2010 International Journal of Economics and Finance  
The study is based on 14 indices relating to the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE), and relates to the period 02/06/1997 to 30/01/2009.  ...  The Chow-Denning test rejects the null of random walk for six indices. The Hinich test rejects the null of pure white noise for full sample period.  ...  The test is based on the idea that the decision regarding the null hypothesis can be made according to the maximum absolute value of the individual variance ratio statistics.  ... 
doi:10.5539/ijef.v2n2p157 fatcat:amyzsmrgabae5iii7dmfcpgvxe

Patient ranking with temporally annotated data

Luca Bonomi, Xiaoqian Jiang
2018 Journal of Biomedical Informatics  
Our experimental evaluations on a real-world dataset demonstrate the efficiency and effectiveness of our approach.  ...  The resulting instances are ranked according to a significance score based on the p-value.  ...  Acknowledgments LB is supported in part by the National Institute of Health (NIH) under award number R21LM012060 and R01GM118574.  ... 
doi:10.1016/j.jbi.2017.12.007 pmid:29277597 pmcid:PMC5880681 fatcat:2dmylcwgv5hafiv3bjjfrfcvbq

Finding sequential patterns with TF-IDF metrics in health-care databases

Zsolt T. Kardkovács, Gábor Kovács
2014 Acta Universitatis Sapientiae: Informatica  
Finding frequent sequential patterns has been defined as finding ordered list of items that occur more times in a database than a user defined threshold.  ...  In this paper, we propose an algorithm that reinterprets the term support on text mining basis.  ...  Acknowledgement Publishing of this work and the research project were funded by the European Union and co-financed by the European Social Fund under the name of ,,MEDICSPHERE -Complex, multipurpose, ICT  ... 
doi:10.1515/ausi-2015-0008 fatcat:ezzzax27wncdxf4zakd4hnz6bm

RQL: A SQL-Like Query Language for Discovering Meaningful Rules

Brice Chardin, Emmanuel Coquery, Marie Pailloux, Jean-Marc Petit
2014 2014 IEEE International Conference on Data Mining Workshop  
The computation of RQL queries is based on a query rewriting technique that pushes as much processing as possible to the underlying DBMS.  ...  This contribution is an attempt to bridge the gap between pattern mining and databases and facilitates the use of data mining techniques by SQL-aware analysts and students.  ...  INTRODUCTION Pattern mining can be seen as an automated part of data exploration.  ... 
doi:10.1109/icdmw.2014.50 dblp:conf/icdm/ChardinCPP14 fatcat:44v4mneppferrowvuoidepwzim

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

2018 Energies  
To overcome these challenges, we propose unsupervised data clustering and frequent pattern mining analysis on energy time series, and Bayesian network prediction for energy usage forecasting.  ...  In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns.  ...  An incremental sequential mining technique to discover correlation patterns among appliances is presented in [1] .  ... 
doi:10.3390/en11020452 fatcat:m3zhtmxgajcoxkyopartzpcglq

COPPER - Constraint OPtimized Prefixspan for Epidemiological Research

Agustin Guevara-Cogorno, Claude Flamand, Hugo Alatrista-Salas
2015 Procedia Computer Science  
Sequential pattern mining, is a data mining technique used to study the temporal evolution of events describing a complex phenomenon.  ...  To tackle this problem, we propose COP, an extension of the PrefixSpan algorithm oriented towards optimizing the relevance of the results obtained in the sequential patterns mining process.  ...  of this work.  ... 
doi:10.1016/j.procs.2015.08.364 fatcat:v2twaxb2bvcn3o5kibkg42w2xi

A New Approach for Handling Null Values in Web Log Using KNN and Tabu Search KNN

Yogendra Kumar Jain, Vivek Suryawanshi
2011 International Journal of Data Mining & Knowledge Management Process  
The result of any mining is successful, only if the dataset under consideration is well preprocessed. One of the important preprocessing steps is handling of null/missing values.  ...  When the data mining procedures deals with the extraction of interesting knowledge from web logs is known as Web usage mining.  ...  Tahir and Ahmed [3] proposed a novel hybrid approach for simultaneous feature selection and feature weighting of K-NN rule based on Tabu Search (TS) heuristic.  ... 
doi:10.5121/ijdkp.2011.1502 fatcat:7u2vwft6szbtzh42afw55d56wi
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