Mining Interesting Infrequent Itemsets from Very Large Data based
... when the count is greater than the minimum support count. The k-itemsets are passed as an input to the mapper function and the mapper outputs, then
the reducer collects all the support counts of an item and
outputs the pairs as a frequent k-itemset to
the Lk.
Othman Yahya et ...
... when the count is greater than the minimum support count. The k-itemsets are passed as an input to the mapper function and the mapper outputs
Identifying Unknown Unknowns in the Open World
... constituent components for the discovery of unknown unknowns across different experimental conditions, providing evidence that the method can be readily applied to discover unknown unknowns in different real-world settings. ...
... constituent components for the discovery of unknown unknowns across different experimental conditions, providing evidence that the method can be readily applied to discover unknown unknowns in different real-world settings. ...
9/12 - Computer and Information Science
... customer, item, supplier, and activity. The data are stored to provide information from a historical perspective (such as from the past 5-10 years) and are typically summarized. For example, rather than storing the details of each sales transaction, the data warehouse may store a summary of the tran ...
... customer, item, supplier, and activity. The data are stored to provide information from a historical perspective (such as from the past 5-10 years) and are typically summarized. For example, rather than storing the details of each sales transaction, the data warehouse may store a summary of the tran ...
a methodology for direct and indirect discrimination
... fairness: classification rules do not guide themselves by personal preferences. Though, at an earlier seem, one realizes that classification rules are actually learned by the system (e.g., loan granting) from the training data. If the training data are essentially biased for or against a particular ...
... fairness: classification rules do not guide themselves by personal preferences. Though, at an earlier seem, one realizes that classification rules are actually learned by the system (e.g., loan granting) from the training data. If the training data are essentially biased for or against a particular ...
Learning Latent Activities from Social Signals with Hierarchical
... The purpose of the feature extraction component is to extract the most relevant and informative features from the raw signals. For example, in vision-based recognition, popular features include SIFT descriptors, silhouettes, contours, edges, pose estimates, velocities and optical flow. For temporal ...
... The purpose of the feature extraction component is to extract the most relevant and informative features from the raw signals. For example, in vision-based recognition, popular features include SIFT descriptors, silhouettes, contours, edges, pose estimates, velocities and optical flow. For temporal ...
Automated linking PUBMED documents with GO terms using SVM
... At training time, NB requires linear time both to the number of training documents and to the number of features and thus its computational requirements are minimal. At classification time, a new example can be also classified in linear time both to the number of features and to the number of classe ...
... At training time, NB requires linear time both to the number of training documents and to the number of features and thus its computational requirements are minimal. At classification time, a new example can be also classified in linear time both to the number of features and to the number of classe ...
指導教授：黃三益 博士 組員：B924020007 王俐文 B924020009
... 1. Compared with other data set, our data are not large enough. So we maybe get some troubles in the modeling process, such as outliers, skew distributions and missing values. 2. The values of the attribute named Media Exposure always show “Good”, so we can not estimate whether this attribute works ...
... 1. Compared with other data set, our data are not large enough. So we maybe get some troubles in the modeling process, such as outliers, skew distributions and missing values. 2. The values of the attribute named Media Exposure always show “Good”, so we can not estimate whether this attribute works ...
Data Mining Algorithms
... reduce irrelevant information—infrequent items are gone frequency descending ordering: more frequent items are more likely to be shared never be larger than the original database (if not count node-links and counts) Experiments demonstrate compression ratios over 100 ...
... reduce irrelevant information—infrequent items are gone frequency descending ordering: more frequent items are more likely to be shared never be larger than the original database (if not count node-links and counts) Experiments demonstrate compression ratios over 100 ...
Data Mining: A Preprocessing Engine
... example, rules generation technique could give low accuracy when it is applied to decimal scaling normalization data set, while it gives much better accuracy when it is applied to z-score or min-max normalization data sets. Designing a task in some way could help in generating better accuracy. A new ...
... example, rules generation technique could give low accuracy when it is applied to decimal scaling normalization data set, while it gives much better accuracy when it is applied to z-score or min-max normalization data sets. Designing a task in some way could help in generating better accuracy. A new ...
Integration of Automated Decision Support Systems with Data
... ABSTRACT—Customer’s behavior and satisfaction are always play important role to increase organization’s growth and market value. Customers are on top priority for the growing organization to build up their businesses. In this paper presents the architecture of Decision Support Systems (DSS) in conne ...
... ABSTRACT—Customer’s behavior and satisfaction are always play important role to increase organization’s growth and market value. Customers are on top priority for the growing organization to build up their businesses. In this paper presents the architecture of Decision Support Systems (DSS) in conne ...
DAta guided approach_article
... nominal data. This technique maps nominal values to numbers in a manner that conveys semantic relationships by assigning order and spacing among the values, helping in the visualization of the natural grouping among the data values in each nominal variable. In the fourth step, we construct an inform ...
... nominal data. This technique maps nominal values to numbers in a manner that conveys semantic relationships by assigning order and spacing among the values, helping in the visualization of the natural grouping among the data values in each nominal variable. In the fourth step, we construct an inform ...
Bayesian rule learning for biomedical data mining
... statistics (or counts) and greatly speeds up the calculations by requiring just one pass through the training data to record the counts. BRL is thus very efficient and runs in O(n2 m) time given n + 1 variables and m training examples, using the default constant values for beam size b and the maximu ...
... statistics (or counts) and greatly speeds up the calculations by requiring just one pass through the training data to record the counts. BRL is thus very efficient and runs in O(n2 m) time given n + 1 variables and m training examples, using the default constant values for beam size b and the maximu ...
A Density-based Hierarchical Clustering Method for Time Series
... The mining result is in the form of a tree of clusters. The internal structure of the data set can be visualized effectively. At last, we conduct an extensive performance study on DHC and some related methods. Our experimental results show that DHC is effective. The mining results match the ground t ...
... The mining result is in the form of a tree of clusters. The internal structure of the data set can be visualized effectively. At last, we conduct an extensive performance study on DHC and some related methods. Our experimental results show that DHC is effective. The mining results match the ground t ...
selection of optimal mining algorithm for outlier detection
... case being assigned to the class most common amongst its K nearest neighbors measured by a distance function. If K = 1, then the case is simply assigned to the class of its nearest neighbor. Choosing the optimal value for K is best done by first inspecting the data. In general, a large K value is mo ...
... case being assigned to the class most common amongst its K nearest neighbors measured by a distance function. If K = 1, then the case is simply assigned to the class of its nearest neighbor. Choosing the optimal value for K is best done by first inspecting the data. In general, a large K value is mo ...
Mining Quantitative Association Rules on Overlapped Intervals
... to “0” and “1”. For quantitative attributes, we keep the original values or transform the values to a standard form, such as Z-Score. We adopt various mapping methods to fit the clustering algorithm. For different data sets, we may use different mapping methods. 2. Apply a clustering algorithm to th ...
... to “0” and “1”. For quantitative attributes, we keep the original values or transform the values to a standard form, such as Z-Score. We adopt various mapping methods to fit the clustering algorithm. For different data sets, we may use different mapping methods. 2. Apply a clustering algorithm to th ...
Diabetes: A Case Study with SAS Enterprise Miner 5.3
... 37 class variables (various demographic, behavioral, and medical attributes) 8 interval variables (BMI, Age, Number of Visits to Doctor, etc.) ...
... 37 class variables (various demographic, behavioral, and medical attributes) 8 interval variables (BMI, Age, Number of Visits to Doctor, etc.) ...
Chapter 12. Outlier Detection
... The index-based, nested-loop based, and grid-based approaches were explored [KN98, KNT00] to speed up distance-based outlier detection. Bay and Schwabacher [BS03] pointed out that the CPU runtime of the nested-loop method is often scalable with respect to the database size. Tao, Xiao, and Zhou [TXZ0 ...
... The index-based, nested-loop based, and grid-based approaches were explored [KN98, KNT00] to speed up distance-based outlier detection. Bay and Schwabacher [BS03] pointed out that the CPU runtime of the nested-loop method is often scalable with respect to the database size. Tao, Xiao, and Zhou [TXZ0 ...
Clustering - NYU Computer Science
... The unit whose weight vector is closest to the current object wins The winner and its neighbors learn by having their weights adjusted SOMs are believed to resemble processing that can occur in the brain Useful for visualizing high-dimensional data in 2- or 3-D space ...
... The unit whose weight vector is closest to the current object wins The winner and its neighbors learn by having their weights adjusted SOMs are believed to resemble processing that can occur in the brain Useful for visualizing high-dimensional data in 2- or 3-D space ...
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.