Online Mining of Maximal Frequent Itemsequences from Data Streams
... The problem of mining frequent itemsets in databases was first addressed by Agrawal et al [2] who have created the apriori-property for frequent itemset mining such that all nonempty sub-itemsets of a frequent itemset must be frequent. During the last decade, many efforts have been made in mining fr ...
... The problem of mining frequent itemsets in databases was first addressed by Agrawal et al [2] who have created the apriori-property for frequent itemset mining such that all nonempty sub-itemsets of a frequent itemset must be frequent. During the last decade, many efforts have been made in mining fr ...
Adaptive clustering Ensembles
... partitions for an ensemble, not all of them easily generalize to adaptive clustering. Our approach extends the studies of ensembles whose partitions are generated via data resampling [5, 6]. Though, intuitively, clustering ensembles generated by other methods can be also boosted. It was shown [9] th ...
... partitions for an ensemble, not all of them easily generalize to adaptive clustering. Our approach extends the studies of ensembles whose partitions are generated via data resampling [5, 6]. Though, intuitively, clustering ensembles generated by other methods can be also boosted. It was shown [9] th ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... Theoretically, Yao‟s general purpose secure circuit-evaluation protocol [7] solves any distributed twoparty privacy-preserving data mining problem. As a practical matter, however, the circuits for even megabyte-sized databases would be intractably large. The alternative has been to find secure speci ...
... Theoretically, Yao‟s general purpose secure circuit-evaluation protocol [7] solves any distributed twoparty privacy-preserving data mining problem. As a practical matter, however, the circuits for even megabyte-sized databases would be intractably large. The alternative has been to find secure speci ...
presentation
... data set, and creates insights to improve further data collection and the development of databases Interaction between data miners and domain experts (ecologist, ecotoxicologist) very important: 1) easily find ‘reliable nonsense’ rules by excluding important variables during the analysis (need for e ...
... data set, and creates insights to improve further data collection and the development of databases Interaction between data miners and domain experts (ecologist, ecotoxicologist) very important: 1) easily find ‘reliable nonsense’ rules by excluding important variables during the analysis (need for e ...
No Slide Title - The University of Texas at Dallas
... data; smoothing applied - SVM: with the parameter settings: one-class SVM with the radial basis function using “gamma” = 0.015 and “nu” = 0.1. ...
... data; smoothing applied - SVM: with the parameter settings: one-class SVM with the radial basis function using “gamma” = 0.015 and “nu” = 0.1. ...
IOSR Journal of Computer Science (IOSR-JCE) e-ISSN: 2278-0661, p-ISSN: 2278-8727 PP 73-78 www.iosrjournals.org
... Data Mining aims at discovering knowledge out of data and presenting it in a form that can be easily understandable to humans. Advanced data mining techniques are used to discover useful knowledge in database and for medical research, particularly in Heart disease prediction. The analysed prediction ...
... Data Mining aims at discovering knowledge out of data and presenting it in a form that can be easily understandable to humans. Advanced data mining techniques are used to discover useful knowledge in database and for medical research, particularly in Heart disease prediction. The analysed prediction ...
Hands-on Data Mining, Digging up clinicaltrials.gov Data with SAS 9
... Before starting complex analysis, one should of course, explore the data. There are many tools for this purpose. One could use PROC UNIVARIATE or more interactive software like JMP® or Enterprise miner®. The latter provides some nice features for exploring data in a more interactive manner, for inst ...
... Before starting complex analysis, one should of course, explore the data. There are many tools for this purpose. One could use PROC UNIVARIATE or more interactive software like JMP® or Enterprise miner®. The latter provides some nice features for exploring data in a more interactive manner, for inst ...
Data Mining – Past, Present and Future – A Typical Survey on Data
... In case of data streams, the number of distinct features or items that exist would be so large which makes even the amount of on cache memory or system memory available not suitable for storing the entire stream data. The main problem with data streams is the speed at which the data streams arrive i ...
... In case of data streams, the number of distinct features or items that exist would be so large which makes even the amount of on cache memory or system memory available not suitable for storing the entire stream data. The main problem with data streams is the speed at which the data streams arrive i ...
PDF
... techniques, data structure and algorithms because we do not have enough space to store this large amount of data. Random sampling, sliding window, histograms, multi resolution methods, sketches and randomized algorithm are basic data structure and methodologies for mining data streams [13]. Classifi ...
... techniques, data structure and algorithms because we do not have enough space to store this large amount of data. Random sampling, sliding window, histograms, multi resolution methods, sketches and randomized algorithm are basic data structure and methodologies for mining data streams [13]. Classifi ...
DATA MINING AND CLUSTERING
... K-Means re-assigns each record in the dataset to the most similar cluster and recalculates the arithmetic mean of all the clusters in the dataset. The arithmetic mean of a cluster is the arithmetic mean of all the records in that cluster. For Example, if a cluster contains two records where the reco ...
... K-Means re-assigns each record in the dataset to the most similar cluster and recalculates the arithmetic mean of all the clusters in the dataset. The arithmetic mean of a cluster is the arithmetic mean of all the records in that cluster. For Example, if a cluster contains two records where the reco ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... In this each row of database represents a transaction which has a transaction identifier (TID), followed by a set of items. 1.1 Apriori Algorithm Apriori algorithm is, the most classical and important algorithm for mining frequent itemsets. Apriori is used to find all frequent itemsets in a given da ...
... In this each row of database represents a transaction which has a transaction identifier (TID), followed by a set of items. 1.1 Apriori Algorithm Apriori algorithm is, the most classical and important algorithm for mining frequent itemsets. Apriori is used to find all frequent itemsets in a given da ...
Exploiting the Data Mining Methodology for Cyber Security Abstract
... security efforts. Some observers suggest that data mining should be used as a means to identify terrorist activities, such as money transfers and communications, and to identify and track individual terrorists themselves, such as through travel and immigration records. Based on the analysis of publi ...
... security efforts. Some observers suggest that data mining should be used as a means to identify terrorist activities, such as money transfers and communications, and to identify and track individual terrorists themselves, such as through travel and immigration records. Based on the analysis of publi ...
CatchingDNStunnelsWithAI
... A talk about Artificial Intelligence, geometry and malicious network traffic. ...
... A talk about Artificial Intelligence, geometry and malicious network traffic. ...
Introduction to Spatial Databases
... Provides simpler set based query operations Example operations: search by region, overlay, nearest neighbor, distance, adjacency, perimeter etc. Uses spatial indices and query optimization to speedup queries over large spatial datasets. ...
... Provides simpler set based query operations Example operations: search by region, overlay, nearest neighbor, distance, adjacency, perimeter etc. Uses spatial indices and query optimization to speedup queries over large spatial datasets. ...
Survey of Clustering Algorithms for Categorization of Patient
... and model based approaches for clustering big data. The three dimensional properties of big data such as volume, velocity and veracity are used to measure the strengths and weaknesses of the algorithm. DENCLUE, BIRCH and OptiGrid are most suitable algorithms for dealing with high dimensional data. T ...
... and model based approaches for clustering big data. The three dimensional properties of big data such as volume, velocity and veracity are used to measure the strengths and weaknesses of the algorithm. DENCLUE, BIRCH and OptiGrid are most suitable algorithms for dealing with high dimensional data. T ...
Data Surveying: Foundations of an Inductive Query Language
... year old males in the database. Its support is simply (age = 25, gender = male), if there is at least one 25 year old male in the database. The cover of our recursive description are all (connected) ancestors "John". It’s support is the set of all (name, child) pairs of all these ancestors. The supp ...
... year old males in the database. Its support is simply (age = 25, gender = male), if there is at least one 25 year old male in the database. The cover of our recursive description are all (connected) ancestors "John". It’s support is the set of all (name, child) pairs of all these ancestors. The supp ...
Bioinformatics System for Gene Diagnostics and Expression Studies
... obtained for all 3 algorithms used, except when microarray gene-expression data were used only. It is felt that for SVM, better results could be obtained using other kernels. Furthermore, better classification results were generally observed when Chinese test sets were shown to Chinesetrained classi ...
... obtained for all 3 algorithms used, except when microarray gene-expression data were used only. It is felt that for SVM, better results could be obtained using other kernels. Furthermore, better classification results were generally observed when Chinese test sets were shown to Chinesetrained classi ...
IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p-ISSN: 2278-8727 PP 29-31 www.iosrjournals.org
... things, the RHS set being liable to happen at whatever point the LHS set happens. Things in quality expression information can incorporate qualities that are very communicated or stifled, and in addition important truths depicting the cell environment of the qualities (e.g. The conclusion of a tumor ...
... things, the RHS set being liable to happen at whatever point the LHS set happens. Things in quality expression information can incorporate qualities that are very communicated or stifled, and in addition important truths depicting the cell environment of the qualities (e.g. The conclusion of a tumor ...
Data Mining and XML - Ulster Institutional Repository
... streams. Both result sets can then be exported to PMML and a third application that is capable of importing both models can utilise the results. The exporting application does not necessarily have to be another knowledge discovery system. Any other analytical application, such as a spreadsheet, or r ...
... streams. Both result sets can then be exported to PMML and a third application that is capable of importing both models can utilise the results. The exporting application does not necessarily have to be another knowledge discovery system. Any other analytical application, such as a spreadsheet, or r ...
Report - UF CISE - University of Florida
... location problems. These include the Euclidean k-medians in which the objective is to minimize the sum of distances to the nearest center and the geometric k-center problem in which the objective is to minimize the maximum distance from every point to its closest center. There are no efficient solut ...
... location problems. These include the Euclidean k-medians in which the objective is to minimize the sum of distances to the nearest center and the geometric k-center problem in which the objective is to minimize the maximum distance from every point to its closest center. There are no efficient solut ...
Nonlinear dimensionality reduction
High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space. If the manifold is of low enough dimension, the data can be visualised in the low-dimensional space.Below is a summary of some of the important algorithms from the history of manifold learning and nonlinear dimensionality reduction (NLDR). Many of these non-linear dimensionality reduction methods are related to the linear methods listed below. Non-linear methods can be broadly classified into two groups: those that provide a mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa), and those that just give a visualisation. In the context of machine learning, mapping methods may be viewed as a preliminary feature extraction step, after which pattern recognition algorithms are applied. Typically those that just give a visualisation are based on proximity data – that is, distance measurements.