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3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Patil, Abhijit Singh, Sanjay Manipal Univ Manipal Inst Technol Dept Informat & Commun Technol Manipal 576104 Karnataka India

ISBN: (纸本)9781479930807

Organizations be it private or public often collect personal information about an individual who are their customers or clients. The personal information of an individual is private and sensitive which has to be secured from data mining algorithm which an adversary may apply to get access to the private information. In this paper we have consider the problem of securing these private and sensitive information when used in random forest classifier in the framework of differential privacy. We have incorporated the concept of differential privacy to the classical random forest algorithm. Experimental results shows that quality functions such as information gain, max operator and gini index gives almost equal accuracy regardless of their sensitivity towards the noise. Also the accuracy of the classical random forest and the differential private random forest is almost equal for different size of datasets. The proposed algorithm works for datasets with categorical as well as continuous attributes.

关键词： data mining data privacy learning (artificial intelligence) Gini index data mining algorithm differential privacy differential private random forest information gain max operator personal information private information sensitive information Accuracy data privacy Indexes Noise Privacy Sensitivity Vegetation

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An accretion based data mining algorithm for identification of sets of correlated neurons

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BMC Neuroscience 2009年第1期10卷 1-2页

作者： Denise Berger Sonja Grün Christian Borgelt Markus Diesmann George Gerstein Neuroinformatics Inst. Biology Freie Universität Berlin 14195 Germany Bernstein Center for Computational Neuroscience Berlin Germany Department of Neuroscience University of Pennsilvania Phildelphia 19104 USA European Center for Soft Computing Mieres (Asturias) 33600 Spain Neuroscience Group RIKEN Brain Science Institute 2-1 Hirosawa Wako-shi 351-0198 Saitama Japan

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Supervised inductive learning with Lotka-Volterra derived models

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KNOWLEDGE AND INFORMATION SYSTEMS 2011年第2期26卷 195-223页

作者： Hovsepian, Karen Anselmo, Peter Mazumdar, Subhasish O Wayne Rollins Res Ctr Taylor Lab Atlanta GA 30322 USA Emory Univ Dept Biol Atlanta GA 30329 USA New Mexico Inst Min & Technol Dept Management Socorro NM 87801 USA New Mexico Inst Min & Technol Dept Comp Sci Socorro NM 87801 USA

We present a classification algorithm built on our adaptation of the Generalized Lotka-Volterra model, well-known in mathematical ecology. The training algorithm itself consists only of computing several scalars, per each training vector, using a single global user parameter and then solving a linear system of equations. Construction of the system matrix is driven by our model and based on kernel functions. The model allows an interesting point of view of kernels' role in the inductive learning process. We describe the model through axiomatic postulates. Finally, we present the results of the preliminary validation experiments.

关键词： Supervised machine learning Classification Lotka-Volterra model data mining algorithm Generalization theory

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PREDICTING CUSTOMER DEMOGRAPHICS IN A MOBILE SOCIAL NETWORK

PREDICTING CUSTOMER DEMOGRAPHICS IN A MOBILE SOCIAL NETWORK

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International Conference on Advances in Social Networks Analysis and mining (ASONAM)

作者： Aarthi, S. Bharanidharan, S. Saravanan, M. Anand, V Sri Venkateswara Coll Engn Dept Comp Sci & Engn Madras Tamil Nadu India Ericsson India Pvt Ltd Ericsson R&D Madras Tamil Nadu India

ISBN: (纸本)9780769543758

A network made of individuals connected based on their communication behaviour using mobile phones can be called as a Mobile Social Network. Mapping and measuring of interactions and flows between people across mobile social networks are being performed extensively in an attempt to understand the intriguing patterns of human behaviour. Such analyses can help in arriving at useful inferences for improving the accuracy of 'Targeted Advertisements'. This paper makes one such attempt to extract the demographics (i.e gender and economic status) of a person based on his/her connectivity in his/ her respective social network(s) and mobile phone usage over a period of time. The need for prediction arises from the fact that, for prepaid users, the demographics are either unavailable or inaccurate. The results produced are evaluated and standardized based on proven statistics pertaining to the nation considered. We use candlestick charts to compare the experimental results.

关键词： data mining algorithm data mining Demographics Prediction Economics Mobile Social Network Analysis Mobile communication Mobile computing Mobile handsets Social network services Targeted Advertisement Telecommunication data Training advertising behavioural sciences computing communication behaviour customer demographics human behaviour mobile computing mobile phone usage mobile social networks social networking (online) targeted advertisement

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Novel computational methods for predicting epitopes of potent and broadly neutralizing HIV-1 antibodies

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Retrovirology 2012年第2期9卷 1-1页

作者： MC Evans A Paquet P Phung A Parikh C Petropoulos T Wrin M Haddad Monogram Biosciences South San Francisco CA USA

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Study on data mining of Time Series

Study on Data Mining of Time Series

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2011年信息技术、服务科学与工程管理国际学术会议

作者： Yong YE Lishui Vocational and Technical College,Zhejiang Lishui,China,323000

Time series have included a series of data materials. These data masteries are the data that is acquired through the passing of the time or the increase of the other variables. Our world is not immutable and frozen. A great amount of variables keep changing with the changes of the time. With the changes of the time,the series of the variable values have formed into a time series. As for the data mining,it is the process to abstract information and knowledge that is people have no idea with in advance but is useful in a potential manner from the data for actual application. The data for actual application here is very large,not complete,equips with noises,randomized,and obscure. This paper mainly analyses the following four matters: the research situation of the data mining of time series,the classifications of the study for data mining of the time series,the study for the data mining algorithm of sequential pattern and the analysis of the clustering of time series. The analysis is expecting to provide references to everyone.

关键词： data mining time series data mining algorithm

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Study on data mining of Time Series

Study on Data Mining of Time Series

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2011年信息技术、服务科学与工程管理国际学术会议

作者： Yong YE Lishui Vocational and Technical College Zhejiang LishuiChina323000

Time series have included a series of data *** data masteries are the data that is acquired through the passing of the time or the increase of the other *** world is not immutable and frozen.A great amount of variables keep changing with the changes of the *** the changes of the time,the series of the variable values have formed into a time *** for the data mining,it is the process to abstract information and knowledge that is people have no idea with in advance but is useful in a potential mannet from the data for actual *** data for actual application here is very large,not complete,equips with noises,randomized,and *** paper mainly analyses the following four matters: the research situation of the data mining of time series,the classifications of the study for data mining of the time series,the study for the data mining algorithm of sequential pattern and the analysis of the clustering of time *** analysis is expecting to provide references to everyone.

关键词： data mining time series data mining algorithm

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One-Class Classification-Based Control Charts for Monitoring Autocorrelated Multivariate Processes

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2010年第3期39卷 461-474页

作者： Kim, Seoung Bum Jitpitaklert, Weerawat Sukchotrat, Thuntee Korea Univ Div Informat Management Engn Seoul South Korea Univ Texas Arlington Dept Ind & Mfg Syst Engn Arlington TX 76019 USA

In recent years, statistical process control (SPC) of multivariate and autocorrelated processes has received a great deal of attention. Modern manufacturing/service systems with more advanced technology and higher production rates can generate complex processes in which consecutive observations are dependent and each variable is correlated. These processes obviously violate the assumption of the independence of each observation that underlies traditional SPC and thus deteriorate the performance of its traditional tools. The popular way to address this issue is to monitor the residualsthe difference between the actual value and the fitted valuewith the traditional SPC approach. However, this residuals-based approach requires two steps: (1) finding the residuals;and (2) monitoring the process. Also, an accurate prediction model is necessary to obtain the uncorrelated residuals. Furthermore, these residuals are not the original values of the observations and consequently may have lost some useful information about the targeted process. The main purpose of this article is to examine the feasibility of using one-class classification-based control charts to handle multivariate and autocorrelated processes. The article uses simulated data to present an analysis and comparison of one-class classification-based control charts and the traditional Hotelling's T2 chart.

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An UpDown Directed Acyclic Graph Approach for Sequential Pattern mining

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2010年第7期22卷 913-928页

作者： Chen, Jinlin CUNY Queens Coll Dept Comp Sci Flushing NY 11367 USA

Traditional pattern growth-based approaches for sequential pattern mining derive length-(k + 1) patterns based on the projected databases of length-k patterns recursively. At each level of recursion, they unidirectionally grow the length of detected patterns by one along the suffix of detected patterns, which needs k levels of recursion to find a length-k pattern. In this paper, a novel data structure, UpDown Directed Acyclic Graph (UDDAG), is invented for efficient sequential pattern mining. UDDAG allows bidirectional pattern growth along both ends of detected patterns. Thus, a length-k pattern can be detected in left perpendicular log(2)k + 1 right perpendicular levels of recursion at best, which results in fewer levels of recursion and faster pattern growth. When minSup is large such that the average pattern length is close to 1, UDDAG and PrefixSpan have similar performance because the problem degrades into frequent item counting problem. However, UDDAG scales up much better. It often outperforms PrefixSpan by almost one order of magnitude in scalability tests. UDDAG is also considerably faster than Spade and LapinSpam. Except for extreme cases, UDDAG uses comparable memory to that of PrefixSpan and less memory than Spade and LapinSpam. Additionally, the special feature of UDDAG enables its extension toward applications involving searching in large spaces.

关键词： data mining algorithm directed acyclic graph performance analysis sequential pattern transaction database

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A Proposal of Spatio-Temporal Pattern Queries

A Proposal of Spatio-Temporal Pattern Queries

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4th International Conference on Complex, Intelligent and Software Intensive Systems (CICIS)

作者： Gorawski, Marcin Jureczek, Pawel Silesian Tech Univ Inst Comp Sci Akad 16 PL-44100 Gliwice Poland

ISBN: (纸本)9780769539676

This paper provides a comprehensive analysis of algorithms for spatio-temporal pattern mining, which helps to select appropriate algorithms for a given Moving Objects database. We can distinguish basic and extended comparing functions. The basic ones correspond to research fields related to pattern mining. The extended comparing functions extend the analysis to additional aspects. All these functions are presented in the context of pattern queries for Moving Objects databases. In addition, comparing functions can be combined into more complex ones and used in frequent pattern mining algorithms in order to obtain various summaries of all analyzed sequences. We also present two algorithms, called VGES (Vertical GSP for ExactSearch) and SES (Spade for ExactSearch), for continuous sequential pattern mining. In the paper different methods of searching for frequent patterns are also compared.

关键词： data mining algorithm pattern query contiguous sequential pattern performance analysis region of interest

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