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Data Warehouse and OLAP Technology
... Star schema: A fact table in the middle connected to a set of dimension tables Snowflake schema: A refinement of star schema where some dimensional hierarchy is normalized into additonal smaller dimension tables, forming a shape similar to ...
... Star schema: A fact table in the middle connected to a set of dimension tables Snowflake schema: A refinement of star schema where some dimensional hierarchy is normalized into additonal smaller dimension tables, forming a shape similar to ...
Unsupervised Entity Resolution on Multi-type Graphs
... To address the aforementioned challenges, we model the observed RDF graph as a multi-type graph and formulate the collective entity resolution as a multi-type graph summarization problem. Particularly, the goal is to transform the original k-type graph into another k-type summary graph composed of s ...
... To address the aforementioned challenges, we model the observed RDF graph as a multi-type graph and formulate the collective entity resolution as a multi-type graph summarization problem. Particularly, the goal is to transform the original k-type graph into another k-type summary graph composed of s ...
GradSeminar - Department of Computing Science
... where m is the number of objects. It is impossible to determine the coordinates of the two objects by knowing only the distance between them. • The Complexity of the OSBR: Communication cost is of the order O(m2), where m is the number of objects under analysis. ...
... where m is the number of objects. It is impossible to determine the coordinates of the two objects by knowing only the distance between them. • The Complexity of the OSBR: Communication cost is of the order O(m2), where m is the number of objects under analysis. ...
Role of Text Mining in Business Intelligence
... This paper includes the combined study of business intelligence and text mining of uncertain data. The data that is used in current business domains is not precise, accurate and complete. Instead, data is considered uncertain and therefore this uncertainty is propagated to the results produced by Bu ...
... This paper includes the combined study of business intelligence and text mining of uncertain data. The data that is used in current business domains is not precise, accurate and complete. Instead, data is considered uncertain and therefore this uncertainty is propagated to the results produced by Bu ...
Intelligente Aufbereitung und Verarbeitung ozeanischer Daten
... (6) Prepare for value extraction and measurement by appropriate data types with right granularity that are fitting to data mining approaches (7) Increasing discrimination of values so that real differences between cases can be detected (8) Rearranging the categories of variables so that they are lin ...
... (6) Prepare for value extraction and measurement by appropriate data types with right granularity that are fitting to data mining approaches (7) Increasing discrimination of values so that real differences between cases can be detected (8) Rearranging the categories of variables so that they are lin ...
Lecture Notes On Data Warehouse
... When a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set ...
... When a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set ...
Representation is Everything: Towards Efficient and Adaptable
... importance with the help of a few parameters. Furthermore, the representation should be such that important data mining operations (such as representing the centroid of a set of strings) should be implementable in a form which is similar to the original data. In many cases, this can allow the use of ...
... importance with the help of a few parameters. Furthermore, the representation should be such that important data mining operations (such as representing the centroid of a set of strings) should be implementable in a form which is similar to the original data. In many cases, this can allow the use of ...
Quality and Complexity Measures for Data Linkage and Deduplication
... to the same entity from several data sets is often required as information from multiple sources needs to be integrated, combined or linked in order to allow more detailed data analysis or mining. The aim of such linkages is to match and aggregate all records relating to the same entity, such as a p ...
... to the same entity from several data sets is often required as information from multiple sources needs to be integrated, combined or linked in order to allow more detailed data analysis or mining. The aim of such linkages is to match and aggregate all records relating to the same entity, such as a p ...
An Effcient Algorithm for Mining Association Rules in Massive Datasets
... An Effcient Algorithm for Mining Association Rules in Massive Datasets Databases (KDD) is one of the most important and interesting research areas in 21st century. Frequent pattern discovery is one of the important techniques in data mining. The application includes Medicine, Telecommunications and ...
... An Effcient Algorithm for Mining Association Rules in Massive Datasets Databases (KDD) is one of the most important and interesting research areas in 21st century. Frequent pattern discovery is one of the important techniques in data mining. The application includes Medicine, Telecommunications and ...
6. Geographic Visualization
... is by presenting a multitude of graphic representations of the data to the user, which allow him or her to interact with the data and change the views in order to gain insight and to draw conclusions, see Keim et al. [439]. Finally, the third driving force for geovisualization is the rise of the Int ...
... is by presenting a multitude of graphic representations of the data to the user, which allow him or her to interact with the data and change the views in order to gain insight and to draw conclusions, see Keim et al. [439]. Finally, the third driving force for geovisualization is the rise of the Int ...
Subspace Clustering and Temporal Mining for Wind
... different subspace projections, while subspace clustering methods are restricted to disjoint sets of objects in different subspace. These subspace projections also can be identified into three major approaches characterized by the underlying cluster definition and parameterization of the resulting c ...
... different subspace projections, while subspace clustering methods are restricted to disjoint sets of objects in different subspace. These subspace projections also can be identified into three major approaches characterized by the underlying cluster definition and parameterization of the resulting c ...
Agents and Data Mining - University of Technology Sydney
... The example is about how to identify a set of “promising” securities to be included in an investment portfolio based on the historical fundamental and technical data about securities. This is a very appropriate domain for data mining for two reasons. First, because the number of available securities ...
... The example is about how to identify a set of “promising” securities to be included in an investment portfolio based on the historical fundamental and technical data about securities. This is a very appropriate domain for data mining for two reasons. First, because the number of available securities ...
Visual-Interactive Preprocessing of Time Series Data
... and compatible as possible. Hence, the data model of our input time series consists of a list of so-called time-value pairs, each containing a time stamp and a corresponding value. This data model is able to represent virtually all possible characteristics of time series data like non-equidistant ti ...
... and compatible as possible. Hence, the data model of our input time series consists of a list of so-called time-value pairs, each containing a time stamp and a corresponding value. This data model is able to represent virtually all possible characteristics of time series data like non-equidistant ti ...
Full Text - ARPN Journals
... patients may include redundant and interrelated symptoms and signs especially when the patients suffer from more than one type of disease of the same category. The physicians may not able to diagnose it correctly. Data mining with intelligent algorithms can be used to tackle the said problem of pred ...
... patients may include redundant and interrelated symptoms and signs especially when the patients suffer from more than one type of disease of the same category. The physicians may not able to diagnose it correctly. Data mining with intelligent algorithms can be used to tackle the said problem of pred ...
role of data mining in education sector
... Regression). They have identified the following important predictors of placement-test: previous test experience, student has a scholarship or not, students’ number of sibling and previous years’ grade point average (GPA). They indicate that decision tree analysis is the best predictor, followed by ...
... Regression). They have identified the following important predictors of placement-test: previous test experience, student has a scholarship or not, students’ number of sibling and previous years’ grade point average (GPA). They indicate that decision tree analysis is the best predictor, followed by ...
White Paper
... repeatable process for model enhancements or recalibrations. Tax laws, for example, are likely to change over time. Analysts need a standard process for updating the models accordingly and deploying new results. The appropriate presentation of results ensures that decision makers actually use the in ...
... repeatable process for model enhancements or recalibrations. Tax laws, for example, are likely to change over time. Analysts need a standard process for updating the models accordingly and deploying new results. The appropriate presentation of results ensures that decision makers actually use the in ...
A Survey on Clustering Based Feature Selection Technique
... relevancy value The generation steps are able to categories different feature selection method according to the way evaluation is carried out. The first four consider as a filter approach and the final one as a wrapper approach. Relief Algorithm Relief is well known and good feature set estimator. F ...
... relevancy value The generation steps are able to categories different feature selection method according to the way evaluation is carried out. The first four consider as a filter approach and the final one as a wrapper approach. Relief Algorithm Relief is well known and good feature set estimator. F ...
... can be used by data mining tasks. A variety of data mining techniques can be applied to this formatted data in the pattern discovery phase, such as clustering, association rule mining, and sequential pattern discovery. The results of the mining phase are transformed into aggregate profiles, suitable ...
068-31: SAS/OR® – Making Sense of Network Data with Network
... A network can be roughly defined as an interconnected group or system; therefore, network data is the information that describes or defines such a system. This information typically consists of two parts—details on the items being connected and information on the connections between these items. Dif ...
... A network can be roughly defined as an interconnected group or system; therefore, network data is the information that describes or defines such a system. This information typically consists of two parts—details on the items being connected and information on the connections between these items. Dif ...
Document
... If we keep one count, it’s ok to use a lot of memory If we have to keep many counts, they should use low memory When learning / mining, we need to keep many counts • Sketching is a good basis for data stream learning / mining ...
... If we keep one count, it’s ok to use a lot of memory If we have to keep many counts, they should use low memory When learning / mining, we need to keep many counts • Sketching is a good basis for data stream learning / mining ...
Data Mining I Data Mining Applications Data Mining
... The database is not used after the 1st pass. Instead, the set Ck’ is used for each step, Ck’ = : each Xk is a potentially frequent
itemset in transaction with id=TID.
At each step Ck’ is generated from Ck-1’ at the
pruning step of constructing Ck and used to
compute Lk.
For small values ...
... The database is not used after the 1st pass. Instead, the set Ck’ is used for each step, Ck’ =
Nonlinear dimensionality reduction
![](https://commons.wikimedia.org/wiki/Special:FilePath/Lle_hlle_swissroll.png?width=300)
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.