Ad hoc Query Support for Very Large Simulation Mesh Data: the
... Splines are essentially a set of basis functions which can be found at the root of many wavelet basis functions and regression approximations. Splines serve as a useful polynomial model of data that originates from the smooth dynamical systems often encountered in scientific applications. Polynomial ...
... Splines are essentially a set of basis functions which can be found at the root of many wavelet basis functions and regression approximations. Splines serve as a useful polynomial model of data that originates from the smooth dynamical systems often encountered in scientific applications. Polynomial ...
Aalborg Universitet Mining Risk Factors in RFID Baggage Tracking Data
... at timestamp Time and the tag stores the information info. Considering only location and time related information, some examples of raw reading records are shown in Fig. 2a. In the table, RID represents the reading identifier. As seen a bag can have several readings at the same location and on the b ...
... at timestamp Time and the tag stores the information info. Considering only location and time related information, some examples of raw reading records are shown in Fig. 2a. In the table, RID represents the reading identifier. As seen a bag can have several readings at the same location and on the b ...
582364 Data mining, 4 cu Lecture 5:
... different combinations of observing X,Y, both or neither of them Contingency table contains sufficient information to compute different interestingness measures Intuitively: if f11 has high support compared to the other cells, the rule is more likely to be interesting than not Contingency tabl ...
... different combinations of observing X,Y, both or neither of them Contingency table contains sufficient information to compute different interestingness measures Intuitively: if f11 has high support compared to the other cells, the rule is more likely to be interesting than not Contingency tabl ...
ROAM: Rule-and Motif-Based Anomaly Detection in Massive Moving
... An outlier is, in general, viewed as “an observation (or a set of observations) which appears to be inconsistent with the remainder of that set of data [2].” However, the term “inconsistent” has many far-reaching implications. The decision is often subjective and depends heavily on the context. Outl ...
... An outlier is, in general, viewed as “an observation (or a set of observations) which appears to be inconsistent with the remainder of that set of data [2].” However, the term “inconsistent” has many far-reaching implications. The decision is often subjective and depends heavily on the context. Outl ...
084
... (gene expression) of thousands of genes. It has 3 phases: Place thousands of different one-strand chunks of RNA in minuscule wells on the surface of a small glass chip Spread genetic material obtained by a cell experiment one wishes to perform Use a laser scanner and computer to measure the am ...
... (gene expression) of thousands of genes. It has 3 phases: Place thousands of different one-strand chunks of RNA in minuscule wells on the surface of a small glass chip Spread genetic material obtained by a cell experiment one wishes to perform Use a laser scanner and computer to measure the am ...
Data, Text and Web Analytics Overview
... adjective), disambiguate and perform fact extraction in the appropriate structure representation, simplify attributes, apply data mining techniques to discover patterns & trends, and evaluate for possible more iteration/refinement ...
... adjective), disambiguate and perform fact extraction in the appropriate structure representation, simplify attributes, apply data mining techniques to discover patterns & trends, and evaluate for possible more iteration/refinement ...
View accepted manuscript: Visualization Techniques for Data Mining
... Many techniques are available to visualize data in three dimensions (Harris, 2000). For example, it is very common to represent data by glyphs (Hoffman and Grinstein, 2001, Fayyad et al, 2001). A glyph can be defined as a three-dimensional object suitable for representing data or subsets of data. Th ...
... Many techniques are available to visualize data in three dimensions (Harris, 2000). For example, it is very common to represent data by glyphs (Hoffman and Grinstein, 2001, Fayyad et al, 2001). A glyph can be defined as a three-dimensional object suitable for representing data or subsets of data. Th ...
Comparison of Cluster Representations from Partial Second
... process in memory. Conversely, a data stream, consisting of temporally ordered points, is transient, continuous, and time-stamped [2]. Thus, data stream clustering aims at separating incoming data, by computation in limited memory, into distinct groups without using historical data, requiring a comp ...
... process in memory. Conversely, a data stream, consisting of temporally ordered points, is transient, continuous, and time-stamped [2]. Thus, data stream clustering aims at separating incoming data, by computation in limited memory, into distinct groups without using historical data, requiring a comp ...
Coupled Attribute Analysis on Numerical Data
... where μj , μk are the respective mean values of aj , ak . However, the Pearson’s correlation coefficient only describes the linear relationship between two variables. It is insufficient if we consider this coefficient just between each pair of continuous attributes. So we expect to expand the numerical ...
... where μj , μk are the respective mean values of aj , ak . However, the Pearson’s correlation coefficient only describes the linear relationship between two variables. It is insufficient if we consider this coefficient just between each pair of continuous attributes. So we expect to expand the numerical ...
Knowledge Discovery in Databases and Libraries
... patterns from textual documents. A text mining technique typically involves text parsing and analysis to transform each unstructured document into an appropriate set of features and subsequently applies one or more data mining techniques for extracting patterns. Finally, patterns are interpreted for ...
... patterns from textual documents. A text mining technique typically involves text parsing and analysis to transform each unstructured document into an appropriate set of features and subsequently applies one or more data mining techniques for extracting patterns. Finally, patterns are interpreted for ...
Providing k-Anonymity in Data Mining
... The first approach toward privacy protection in data mining was to perturb the input (the data) before it is mined [4]. Thus, it was claimed, the original data would remain secret, while the added noise would average out in the output. This approach has the benefit of simplicity. At the same time, i ...
... The first approach toward privacy protection in data mining was to perturb the input (the data) before it is mined [4]. Thus, it was claimed, the original data would remain secret, while the added noise would average out in the output. This approach has the benefit of simplicity. At the same time, i ...
CS490D: Introduction to Data Mining Chris Clifton
... Data mining Approach • Use information from text fields to supplement current structured fields by extracting features from text in accident reports • Build a human-error classifier directly from data – Use expert to provide class labels for events of interest such as ‗slips‘, ‗mistakes‘ and ‗other‘ ...
... Data mining Approach • Use information from text fields to supplement current structured fields by extracting features from text in accident reports • Build a human-error classifier directly from data – Use expert to provide class labels for events of interest such as ‗slips‘, ‗mistakes‘ and ‗other‘ ...
Data Mining System, Functionalities and Applications: A
... algorithms are efficient enough to control these massive data sets then they must be scalable. Future data mining should be a continuous, online process instead of one time tiny process. The said scalability also warrants the execution of novel data structure to access individual records in a smooth ...
... algorithms are efficient enough to control these massive data sets then they must be scalable. Future data mining should be a continuous, online process instead of one time tiny process. The said scalability also warrants the execution of novel data structure to access individual records in a smooth ...
Active Learning to Maximize Area Under the ROC Curve
... The “Closest Sampling” method [24, 26] (sometimes called “uncertainty sampling”) can be thought of as a heuristic to shrink the version space. It greedily selects points that are closest to the current decision boundary. The intuition behind this is that points that are closest to the current decisi ...
... The “Closest Sampling” method [24, 26] (sometimes called “uncertainty sampling”) can be thought of as a heuristic to shrink the version space. It greedily selects points that are closest to the current decision boundary. The intuition behind this is that points that are closest to the current decisi ...
MoveMine: mining moving object databases
... The MoveMine system integrates data mining functions presented in Section 3. The methods embedded in the system are novel, practical, and derived from recent research. To demonstrate its effectiveness, a large collection of various real data sets from different resources are used. Moreover, we also co ...
... The MoveMine system integrates data mining functions presented in Section 3. The methods embedded in the system are novel, practical, and derived from recent research. To demonstrate its effectiveness, a large collection of various real data sets from different resources are used. Moreover, we also co ...
Recent Progress on Selected Topics in Database Research
... the query by synthesizing source views, called answering queries using views. Many techniques have been developed to solve this problem [9, 12], and these techniques can also be used in other database applications such as data warehousing and query optimization. Another approach to data integration, ...
... the query by synthesizing source views, called answering queries using views. Many techniques have been developed to solve this problem [9, 12], and these techniques can also be used in other database applications such as data warehousing and query optimization. Another approach to data integration, ...
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.