Stream Data Mining
... All raw data sets which are initially prepared for data mining are often large; many are related to humans and have the potential for being messy [19]. Real-world databases are subject to noise, missing, and inconsistent data due to their typically huge size, often several gigabytes or more. Data pr ...
... All raw data sets which are initially prepared for data mining are often large; many are related to humans and have the potential for being messy [19]. Real-world databases are subject to noise, missing, and inconsistent data due to their typically huge size, often several gigabytes or more. Data pr ...
data warehousing and data mining applications for
... Abstract— Meteorology is an important area of practice and research of the atmospheric considerations that focuses on weather conditions. In current global scientific environment the atmospheric data and its information is one of the most valuable asset for scientists and researchers to evaluate the ...
... Abstract— Meteorology is an important area of practice and research of the atmospheric considerations that focuses on weather conditions. In current global scientific environment the atmospheric data and its information is one of the most valuable asset for scientists and researchers to evaluate the ...
Text Mining - Computer Science Intranet
... If a word appears in all classes evenly, then it doesn't distinguish any particular class, and is not useful for classification and can be ignored. eg 'the' Equally, a word that appears in only one document will be perfectly discriminating, but also probably over-fitting. Words that appear in most d ...
... If a word appears in all classes evenly, then it doesn't distinguish any particular class, and is not useful for classification and can be ignored. eg 'the' Equally, a word that appears in only one document will be perfectly discriminating, but also probably over-fitting. Words that appear in most d ...
On the Development of Data Mining Certificate Program at the University of Central Florida With SAS
... building equipped with high-end personal computers, server, and color printer. The lab is open to all students weekdays. If students need to work on their projects during the weekend, they can make arrangement with the lab assistant. STUDIO STYLE MULTIMEDIA CLASSROOM Since the instruction is worksho ...
... building equipped with high-end personal computers, server, and color printer. The lab is open to all students weekdays. If students need to work on their projects during the weekend, they can make arrangement with the lab assistant. STUDIO STYLE MULTIMEDIA CLASSROOM Since the instruction is worksho ...
Optimization of Naïve Bayes Data Mining Classification Algorithm
... however, the performance of Naive Bayes classification algorithm suffers in the domains (data set) that involve correlated features. [Correlated features are the features which have a mutual relationship or connection with each other. As correlated features are related to each other, they are measur ...
... however, the performance of Naive Bayes classification algorithm suffers in the domains (data set) that involve correlated features. [Correlated features are the features which have a mutual relationship or connection with each other. As correlated features are related to each other, they are measur ...
Discovering Correlated Subspace Clusters in 3D
... support metric, which requires the values in the cluster to have high occurrences together, but they do not consider the second characteristic. Brin et al. [2] proposed the lift metric, which measures the second characteristic, but is biased towards values with low occurrences. Moise and Sander [3] ...
... support metric, which requires the values in the cluster to have high occurrences together, but they do not consider the second characteristic. Brin et al. [2] proposed the lift metric, which measures the second characteristic, but is biased towards values with low occurrences. Moise and Sander [3] ...
re-mining association mining results through visualization, data
... are comparing, such as items, stores, suppliers, etc., and gain actionable insights on how to improve inefficient entities. These entities being compared are referred to as DMUs (Decision Making Units). In DEA, calculation of efficiency scores (the primary benchmark metric) for the DMUs is based on ...
... are comparing, such as items, stores, suppliers, etc., and gain actionable insights on how to improve inefficient entities. These entities being compared are referred to as DMUs (Decision Making Units). In DEA, calculation of efficiency scores (the primary benchmark metric) for the DMUs is based on ...
Data Mining Technologies - College of Business « UNT
... then relevant sets of three or four. • These are then pruned by removing those that occur infrequently. • In an environment like a grocery store, where customers commonly buy over 100 items, rules could involve as many as 10 items. ...
... then relevant sets of three or four. • These are then pruned by removing those that occur infrequently. • In an environment like a grocery store, where customers commonly buy over 100 items, rules could involve as many as 10 items. ...
preprint
... k-NN-weight model [12] uses the sum of distances to all objects within the set of k nearest neighbors (called the weight) as an outlier degree. While these models actually only use distances, the intuition is typically discussed with Euclidean data space in mind. In these distance-based approaches, ...
... k-NN-weight model [12] uses the sum of distances to all objects within the set of k nearest neighbors (called the weight) as an outlier degree. While these models actually only use distances, the intuition is typically discussed with Euclidean data space in mind. In these distance-based approaches, ...
Temporal data - ResearchGate
... Length of the series should be a power of 2: zero pad the series! The Haar transform: all the difference values dl,i at every level l and offset i (n-1) difference, plus the smooth component sL,0 at the last level Computational complexity is O(n) Iyad Batal ...
... Length of the series should be a power of 2: zero pad the series! The Haar transform: all the difference values dl,i at every level l and offset i (n-1) difference, plus the smooth component sL,0 at the last level Computational complexity is O(n) Iyad Batal ...
Slides: Clustering review
... error – One easy way to reduce SSE is to increase K, the number of clusters A good clustering with smaller K can have a lower SSE than a poor clustering with higher K ...
... error – One easy way to reduce SSE is to increase K, the number of clusters A good clustering with smaller K can have a lower SSE than a poor clustering with higher K ...
On A New Scheme on Privacy Preserving Data Classification ∗
... Based on the model of data miners, we review the randomization approach, which is currently used to preserve privacy in classification. We also point out the problems associated with the randomization approach that motivates us to design a new privacy preserving scheme on data classification. To pre ...
... Based on the model of data miners, we review the randomization approach, which is currently used to preserve privacy in classification. We also point out the problems associated with the randomization approach that motivates us to design a new privacy preserving scheme on data classification. To pre ...
Frequent Item Sets
... ● Designed to reduce the number of pairs that need to be counted ● How? hint: There is no such thing as a free lunch ● Perform 2 passes over data ...
... ● Designed to reduce the number of pairs that need to be counted ● How? hint: There is no such thing as a free lunch ● Perform 2 passes over data ...
Data Cube
... Sorting, hashing, and grouping operations are applied to the dimension attributes in order to reorder and cluster related tuples Aggregates may be computed from previously computed aggregates, rather than from the base fact table ...
... Sorting, hashing, and grouping operations are applied to the dimension attributes in order to reorder and cluster related tuples Aggregates may be computed from previously computed aggregates, rather than from the base fact table ...
Mining Concept-Drifting Data Streams using Ensemble Classifiers
... optimum decision boundary during each time interval. The problem is: after the arrival of S2 at time t3 , what part of the training data should still remain influential in the current model so that the data arriving right after t3 can be most accurately classified? On one hand, in order to reduce th ...
... optimum decision boundary during each time interval. The problem is: after the arrival of S2 at time t3 , what part of the training data should still remain influential in the current model so that the data arriving right after t3 can be most accurately classified? On one hand, in order to reduce th ...
Discovering Rules with Concept Hierarchies
... 3.5. Interpretation of the Induced Rules The induced rules can be interpreted as classification rules. Thus, to use the induced rules to classify new examples, NETUNO-HC employ an interpretation in which all rules are tried and only those that cover the example are collected. If a collision occurs ...
... 3.5. Interpretation of the Induced Rules The induced rules can be interpreted as classification rules. Thus, to use the induced rules to classify new examples, NETUNO-HC employ an interpretation in which all rules are tried and only those that cover the example are collected. If a collision occurs ...
Data Mining - Soft Computing and Intelligent Information Systems
... Introduction to Data Mining and Knowledge Discovery Data Preparation Introduction to Prediction, Classification, Clustering and Association Data Mining - From the Top 10 Algorithms to the New Challenges Introduction to Soft Computing. Focusing our attention in Fuzzy Logic and Evolutionary Computatio ...
... Introduction to Data Mining and Knowledge Discovery Data Preparation Introduction to Prediction, Classification, Clustering and Association Data Mining - From the Top 10 Algorithms to the New Challenges Introduction to Soft Computing. Focusing our attention in Fuzzy Logic and Evolutionary Computatio ...
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.