Scalable Techniques for Mining Causal ...
... presence or absence of that item. In this view, a basket is simply a boolean vector of values assigned to these variables. The market basket problem is to find “interesting” patterns in this data. The bulk of past research has concentrated on patterns that are called association rules, of the type: ...
... presence or absence of that item. In this view, a basket is simply a boolean vector of values assigned to these variables. The market basket problem is to find “interesting” patterns in this data. The bulk of past research has concentrated on patterns that are called association rules, of the type: ...
Compression, Clustering and Pattern Discovery in Very High
... probability and signal processing. In the rest of this section, we summarize commonly used orthogonal and non-orthogonal matrix transformations, latent structure analysis and their applications in data analysis and explore alternate approaches for binary datasets. A. Orthogonal and Non-Orthogonal Ma ...
... probability and signal processing. In the rest of this section, we summarize commonly used orthogonal and non-orthogonal matrix transformations, latent structure analysis and their applications in data analysis and explore alternate approaches for binary datasets. A. Orthogonal and Non-Orthogonal Ma ...
A Scalable Parallel Classifier for Data Mining
... Unfortunately, for the remaining attribute lists of the node (CarType in our example), we have no test that we can apply to the attribute values to decide how to divide the records. We therefore work with the rids. As we partition the list of the splitting attribute (i.e. Age), we insert the rids of ...
... Unfortunately, for the remaining attribute lists of the node (CarType in our example), we have no test that we can apply to the attribute values to decide how to divide the records. We therefore work with the rids. As we partition the list of the splitting attribute (i.e. Age), we insert the rids of ...
Big Crisis Data - Chapter 6
... through a push/subscription/live API (see Section 2.2), and arrives after a delay in the order of a few seconds (i.e., with low latency), or a few hundred milliseconds (i.e., in “real-time”). An example of a live data analysis application could be to generate alerts of important events in a disaster ...
... through a push/subscription/live API (see Section 2.2), and arrives after a delay in the order of a few seconds (i.e., with low latency), or a few hundred milliseconds (i.e., in “real-time”). An example of a live data analysis application could be to generate alerts of important events in a disaster ...
Design and implementation of a data mining grid
... However, the number and diversity of these data items may cause the opposite effect, that is, they can hide the underlying information, avoiding the understanding of such information. For this reason, it is necessary to use data abstractions in order to discover the essence of the information. Data ...
... However, the number and diversity of these data items may cause the opposite effect, that is, they can hide the underlying information, avoiding the understanding of such information. For this reason, it is necessary to use data abstractions in order to discover the essence of the information. Data ...
Conceptual Clustering Categorical Data with Uncertainty
... automatic data integration. For example deep web data in the form of dynamic HTML pages can be used to generate related datasets. This is a challenging problem. Often the mapping from information in a web page to a set of attributes is unclear. It may be known that a page contains prices for several ...
... automatic data integration. For example deep web data in the form of dynamic HTML pages can be used to generate related datasets. This is a challenging problem. Often the mapping from information in a web page to a set of attributes is unclear. It may be known that a page contains prices for several ...
A Framework for Trajectory Data Preprocessing for Data Mining
... The increasing use of GPS devices to capture the position of moving objects demands tools for the efficient analysis of large amounts of data referenced in space and time. Current analysis over trajectories of moving objects have basically to be performed manually. Another problem is that most techn ...
... The increasing use of GPS devices to capture the position of moving objects demands tools for the efficient analysis of large amounts of data referenced in space and time. Current analysis over trajectories of moving objects have basically to be performed manually. Another problem is that most techn ...
Conceptual Clustering Categorical Data with Uncertainty
... automatic data integration. For example deep web data in the form of dynamic HTML pages can be used to generate related datasets. This is a challenging problem. Often the mapping from information in a web page to a set of attributes is unclear. It may be known that a page contains prices for several ...
... automatic data integration. For example deep web data in the form of dynamic HTML pages can be used to generate related datasets. This is a challenging problem. Often the mapping from information in a web page to a set of attributes is unclear. It may be known that a page contains prices for several ...
Constrained itemset mining on a sequence of incoming data blocks
... has been devoted to the implementation of frequent itemset algorithms in and for relational database management systems. However, the demand for integration of data mining tools into the existing database management systems is significant. Coupling with database systems has been at best loose, and a ...
... has been devoted to the implementation of frequent itemset algorithms in and for relational database management systems. However, the demand for integration of data mining tools into the existing database management systems is significant. Coupling with database systems has been at best loose, and a ...
A Survey on Privacy Preserving Time
... Geometric transformation-based perturbation approaches exploit rotation, translation, and scaling and perturb all dimensions at the same time in order to preserve correlations among dimensions unlike the existing perturbation techniques. Chen et al.[21] have proposed a rotation perturbation techniqu ...
... Geometric transformation-based perturbation approaches exploit rotation, translation, and scaling and perturb all dimensions at the same time in order to preserve correlations among dimensions unlike the existing perturbation techniques. Chen et al.[21] have proposed a rotation perturbation techniqu ...
forecasting knowledge extraction by computational intelligence
... In the first step of the proposed methodology the most appropriate input parameters were selected. In order to determine the most influential input attribute in predicting the output there were testing the possibilities of having as inputs 1, 2, 3 or 4 nodes. The results show that the most appropria ...
... In the first step of the proposed methodology the most appropriate input parameters were selected. In order to determine the most influential input attribute in predicting the output there were testing the possibilities of having as inputs 1, 2, 3 or 4 nodes. The results show that the most appropria ...
FP-Outlier: Frequent Pattern Based Outlier Detection
... Deviation-based techniques identify outliers by inspecting the characteristics of objects and consider an object as an outlier if the object deviates from these features [9]. Breunig et al. [3] introduced the concept of “local outlier”. The outlier rank of a data object is determined by taking into ...
... Deviation-based techniques identify outliers by inspecting the characteristics of objects and consider an object as an outlier if the object deviates from these features [9]. Breunig et al. [3] introduced the concept of “local outlier”. The outlier rank of a data object is determined by taking into ...
Proposal of knowledge discovery platform for big data
... enabling direct communication with upper control levels. Each parameter of manufacturing process is represented by a large amount of production data applicable in information or control systems at various levels. Despite the fact that most of manufacturing companies gather these data, they are not f ...
... enabling direct communication with upper control levels. Each parameter of manufacturing process is represented by a large amount of production data applicable in information or control systems at various levels. Despite the fact that most of manufacturing companies gather these data, they are not f ...
Boolean Property Encoding for Local Set Pattern
... data appears as a promising and complementary approach. The last 5 years, a major research sub-domain in data mining has concerned the design of efficient and complete constraint-based mining tools on boolean data, also called transactional data by some authors. The completeness assumption means that ...
... data appears as a promising and complementary approach. The last 5 years, a major research sub-domain in data mining has concerned the design of efficient and complete constraint-based mining tools on boolean data, also called transactional data by some authors. The completeness assumption means that ...
Machine Learning
... • General idea: the analysis of large amounts of data (and therefore efficiency is an issue) • Interfaces several areas, notably machine learning and database systems • Lots of perspectives: – ML: learning where efficiency matters – DBMS: extended techniques for analysis of raw data, automatic produ ...
... • General idea: the analysis of large amounts of data (and therefore efficiency is an issue) • Interfaces several areas, notably machine learning and database systems • Lots of perspectives: – ML: learning where efficiency matters – DBMS: extended techniques for analysis of raw data, automatic produ ...
Borders: An Efficient Algorithm for Association Generation in
... Abstract. We consider the problem of finding association rules in a database with binary attributes. Most algorithms for finding such rules assume that all the data is available at the start of the data mining session. In practice, the data in the database may change over time, with records being ad ...
... Abstract. We consider the problem of finding association rules in a database with binary attributes. Most algorithms for finding such rules assume that all the data is available at the start of the data mining session. In practice, the data in the database may change over time, with records being ad ...
obtaining best parameter values for accurate classification
... relatively well. CMAR is generally less sensitive than CBA to the choice of thresholds, but both methods give very poor results when, as in the cases of chess and letrecog, the chosen confidence threshold is too high, and CMAR performs relatively poorly for led7 for the same reason. The extreme case ...
... relatively well. CMAR is generally less sensitive than CBA to the choice of thresholds, but both methods give very poor results when, as in the cases of chess and letrecog, the chosen confidence threshold is too high, and CMAR performs relatively poorly for led7 for the same reason. The extreme case ...
Research of data mining system Xu Ruiying
... find out implicit and useful knowledge from a large number of data set. The visualization of data mining mainly includes the visualization of data, mining process and mining model. The current visualization techniques mainly include the traditional geometry method (such as graph, histogram, scatter ...
... find out implicit and useful knowledge from a large number of data set. The visualization of data mining mainly includes the visualization of data, mining process and mining model. The current visualization techniques mainly include the traditional geometry method (such as graph, histogram, scatter ...
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.