Meta-data and Data Mart solutions for better understanding for data
... improve their works such as: ETL: There are many companies develop ETL tools, such as Oracle and SAP. ETL is extract, transform and load that mean first the data extracts from multi databases, storage and documents then transfer this data into suitable form for new common warehouse by using special ...
... improve their works such as: ETL: There are many companies develop ETL tools, such as Oracle and SAP. ETL is extract, transform and load that mean first the data extracts from multi databases, storage and documents then transfer this data into suitable form for new common warehouse by using special ...
Knowledge Discovery in Databases
... – “We don’t store all the data as that would be impractical. Instead, from the collisions we run, we only keep the few pieces that are of interest, the rare events that occur, which our filters spot and send on over the network,”. – CERN stores 25PB of selected data each year which is the equivalen ...
... – “We don’t store all the data as that would be impractical. Instead, from the collisions we run, we only keep the few pieces that are of interest, the rare events that occur, which our filters spot and send on over the network,”. – CERN stores 25PB of selected data each year which is the equivalen ...
Database Clustering and Summary Generation
... less concerned with the the preprocessing step. KDD involves the collaboration between multiple disciplines: namely, statistics, AI, visualization, and databases. KDD employs non-traditional data analysis techniques (neural networks, association rules, decision trees, fuzzy logic, evolutionary compu ...
... less concerned with the the preprocessing step. KDD involves the collaboration between multiple disciplines: namely, statistics, AI, visualization, and databases. KDD employs non-traditional data analysis techniques (neural networks, association rules, decision trees, fuzzy logic, evolutionary compu ...
Use of Data Mining for Validation and Verification of Maritime Cargo
... shipment. Mining the Web is challenging not only because of its size—it is the largest publicly accessible data source in the world—but also because the Web includes diverse formats. Information is redundant and heterogeneous (i.e., the same information is provided by many sources in different conte ...
... shipment. Mining the Web is challenging not only because of its size—it is the largest publicly accessible data source in the world—but also because the Web includes diverse formats. Information is redundant and heterogeneous (i.e., the same information is provided by many sources in different conte ...
A Statistical Perspective of Data Mining
... preferences? In a word: nothing. But through the clever application of information technology, even the largest enterprise can come surprisingly close. In large commercial enterprises, the first step noticing what the customer does - has already largely been automated. On-line transaction processing ...
... preferences? In a word: nothing. But through the clever application of information technology, even the largest enterprise can come surprisingly close. In large commercial enterprises, the first step noticing what the customer does - has already largely been automated. On-line transaction processing ...
Big Data Mining: Challenges, Technologies, Tools and Applications
... Big data is a data with large size means it has large volume, velocity and variety. Now a day’s big data is expanding in a various science and engineering fields. And so there are many challenges to manage and analyse big data using various tools. This paper introduces the big data and its Character ...
... Big data is a data with large size means it has large volume, velocity and variety. Now a day’s big data is expanding in a various science and engineering fields. And so there are many challenges to manage and analyse big data using various tools. This paper introduces the big data and its Character ...
MBPD: Motif-Based Period Detection
... wxwz, the sequence wx is periodic with period p = 4; and the partial periodic pattern wx ** exists in T, where * denotes a variable symbol. • a time series exhibits symbol periodicity if at most one symbol is repeated periodically. For example, in time series T = xyz xzy xxy xyy, symbol x is periodi ...
... wxwz, the sequence wx is periodic with period p = 4; and the partial periodic pattern wx ** exists in T, where * denotes a variable symbol. • a time series exhibits symbol periodicity if at most one symbol is repeated periodically. For example, in time series T = xyz xzy xxy xyy, symbol x is periodi ...
Data warehousing and data mining
... Presentation: decision-tree, classification rule, neural network Prediction: Predict some unknown or missing numerical values ...
... Presentation: decision-tree, classification rule, neural network Prediction: Predict some unknown or missing numerical values ...
chap3_data_exploration_and_OLAP
... – They partition the plane into regions of similar values – The contour lines that form the boundaries of these regions connect points with equal values – The most common example is contour maps of elevation – Can also display temperature, rainfall, air pressure, etc. ...
... – They partition the plane into regions of similar values – The contour lines that form the boundaries of these regions connect points with equal values – The most common example is contour maps of elevation – Can also display temperature, rainfall, air pressure, etc. ...
Large-Scale Dataset Incremental Association Rules Mining Model
... rule mining is often a huge centralized or distributed data sources. If the single machine for association rules mining, storage capacity and the mining efficiency is bound to become a bottleneck in the process of mining, which can't meet the needs of large data mining. But with the rapid developmen ...
... rule mining is often a huge centralized or distributed data sources. If the single machine for association rules mining, storage capacity and the mining efficiency is bound to become a bottleneck in the process of mining, which can't meet the needs of large data mining. But with the rapid developmen ...
comparison of filter based feature selection algorithms
... feature selection methods as to their effectiveness in preprocessing input data for inducing decision trees. They used realworld data to evaluate these feature selection methods. Results from this study show that inter-class distance measures result in better performance compared to probabilistic me ...
... feature selection methods as to their effectiveness in preprocessing input data for inducing decision trees. They used realworld data to evaluate these feature selection methods. Results from this study show that inter-class distance measures result in better performance compared to probabilistic me ...
online social network mining: current trends and research issues
... elementary influence propagation models namely, the Independent Cascade (IC) model and the Linear Threshold (LT) model. These models consider a node to be either active or inactive for a given timestamp. An active node can be a customer who already purchased a product or an adopter of the innovation ...
... elementary influence propagation models namely, the Independent Cascade (IC) model and the Linear Threshold (LT) model. These models consider a node to be either active or inactive for a given timestamp. An active node can be a customer who already purchased a product or an adopter of the innovation ...
There's No Such Thing as Normal Clinical Trials Data, or Is There'
... The above program only includes four lab parameters (which fit nicely on the page). In the event you are displaying more than four to five lab parameters a macro can be generated where you tell the program which lab parameters to display. In the code in Example-4, note the LABVALN variable is used a ...
... The above program only includes four lab parameters (which fit nicely on the page). In the event you are displaying more than four to five lab parameters a macro can be generated where you tell the program which lab parameters to display. In the code in Example-4, note the LABVALN variable is used a ...
There's No Such Thing as Normal Clinical Trials Data, or Is There?
... PROC REPORT code for both data structures would have to be modified slightly, so the difference would be less apparent. However, if your standard program code was designed in such a way that the PROC REPORT code was written based on the number of transposed variables created, then you would NOT have ...
... PROC REPORT code for both data structures would have to be modified slightly, so the difference would be less apparent. However, if your standard program code was designed in such a way that the PROC REPORT code was written based on the number of transposed variables created, then you would NOT have ...
Data Mining - Faculty of Computer Science
... ¤ A collection of tables. Each one has a unique name ¤ A table contains a set of attributes (columns) & tuples (rows). ¤ Each object in a relational table has a unique key and is described Costumers by a set of attribute values. cust_Id Name age income ¤ Data are accessed using database ...
... ¤ A collection of tables. Each one has a unique name ¤ A table contains a set of attributes (columns) & tuples (rows). ¤ Each object in a relational table has a unique key and is described Costumers by a set of attribute values. cust_Id Name age income ¤ Data are accessed using database ...
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.