A Universal Data Pre-processing System
... these depend on quality of input data. Noisy and erroneous data makes these algorithms useless. Before processing, data needs to be investigated and preprocessed. This paper specifies a set of requirements on a data pre-processing system, which should be satisfied in order to provide a complete and ...
... these depend on quality of input data. Noisy and erroneous data makes these algorithms useless. Before processing, data needs to be investigated and preprocessed. This paper specifies a set of requirements on a data pre-processing system, which should be satisfied in order to provide a complete and ...
Reality Mining: Data Mining or something else?
... From the data mining point-of-view, reality mining deals with the most challenging data mining problems as defined in [12]. In particular, it tackles the issues of “scaling up for high dimensional data/high speed streams”, “mining sequence data and time series data”, and “data mining in a network se ...
... From the data mining point-of-view, reality mining deals with the most challenging data mining problems as defined in [12]. In particular, it tackles the issues of “scaling up for high dimensional data/high speed streams”, “mining sequence data and time series data”, and “data mining in a network se ...
NSF Annual report: 2007-2008 - users.cs.umn.edu
... a time series. Since the model does not replicate the entire graph for every instant of time, it uses less memory and the algorithms for common operations are computationally more efficient than for time expanded networks. One important query on spatio-temporal networks is the computation of shortes ...
... a time series. Since the model does not replicate the entire graph for every instant of time, it uses less memory and the algorithms for common operations are computationally more efficient than for time expanded networks. One important query on spatio-temporal networks is the computation of shortes ...
chapter-23 mining complex types of data
... An important feature of object-relational and object-oriented databases is their capability of storing, accessing and modeling complex structure-valued data, such as set-valued and list-valued data end data with nested structures. Let’s start by having a look at the generalization of set-valued and ...
... An important feature of object-relational and object-oriented databases is their capability of storing, accessing and modeling complex structure-valued data, such as set-valued and list-valued data end data with nested structures. Let’s start by having a look at the generalization of set-valued and ...
Simulation of Fuzzy Multiattribute Models
... There were a total of eight explanatory variables used in these three decision trees. The same runs were made for the categorical data reflecting grey related input. Four unique decision trees were obtained, with formulas again given below. A total of seven explanatory variables were used in these f ...
... There were a total of eight explanatory variables used in these three decision trees. The same runs were made for the categorical data reflecting grey related input. Four unique decision trees were obtained, with formulas again given below. A total of seven explanatory variables were used in these f ...
A new data clustering approach for data mining in large databases
... incorporate a priori knowledge regarding the global shape or size of clusters. As a result, they cannot always separate overlapping clusters. In addition, hierarchical clustering is static, and points committed to a given cluster in the early stages cannot move to a different cluster. Prototype-base ...
... incorporate a priori knowledge regarding the global shape or size of clusters. As a result, they cannot always separate overlapping clusters. In addition, hierarchical clustering is static, and points committed to a given cluster in the early stages cannot move to a different cluster. Prototype-base ...
A New Privacy-Preserving Distributed k
... on a typical data set are in Figure 2. Recluster does very well in identifying cluster centers, even in the presence of noise. When averaged over all data sets that used the uniform distribution, more than two runs out of the 10 runs of k-means algorithm resulted in the misidentification of cluster ...
... on a typical data set are in Figure 2. Recluster does very well in identifying cluster centers, even in the presence of noise. When averaged over all data sets that used the uniform distribution, more than two runs out of the 10 runs of k-means algorithm resulted in the misidentification of cluster ...
Applying Data Mining Classification Techniques for
... from 10 fold cross validation datasets. As part of the classification process, the classifier generated by each classification technique must be applied to the unseen data. This process is known as the use of model phase, which shows the percentage of correctly classified instances or the accuracy o ...
... from 10 fold cross validation datasets. As part of the classification process, the classifier generated by each classification technique must be applied to the unseen data. This process is known as the use of model phase, which shows the percentage of correctly classified instances or the accuracy o ...
1 - Statistical Aspects of Data Mining
... What is Cluster Analysis? z “Cluster analysis divides data into groups (clusters) that are meaningful, useful, or both” (page 487) z It is similar to classification, only now we don’t know the “answer” (we don’t have the labels) z For this reason, clustering is often called unsupervised learning wh ...
... What is Cluster Analysis? z “Cluster analysis divides data into groups (clusters) that are meaningful, useful, or both” (page 487) z It is similar to classification, only now we don’t know the “answer” (we don’t have the labels) z For this reason, clustering is often called unsupervised learning wh ...
A Secure Information Hiding Approach in Cloud Using LSB
... of data. The aim is to discover patterns or fabricate models using particular algorithms from various scientific disciplines including artificial intelligence, machine learning, database systems and statistics. The data mining tasks can be classified into two categories with respect to this definiti ...
... of data. The aim is to discover patterns or fabricate models using particular algorithms from various scientific disciplines including artificial intelligence, machine learning, database systems and statistics. The data mining tasks can be classified into two categories with respect to this definiti ...
Chapter 0 - KSU Web Home
... information visualization from scientific visualization. • Information visualization: categorical variables and the discovery of patterns, trends, clusters, outliers, and gaps • Scientific visualization: continuous variables, volumes and surfaces • Information visualization provides compact graphica ...
... information visualization from scientific visualization. • Information visualization: categorical variables and the discovery of patterns, trends, clusters, outliers, and gaps • Scientific visualization: continuous variables, volumes and surfaces • Information visualization provides compact graphica ...
PDF
... Figure 2. Accuracy B. Error rate The error rate of the algorithm demonstrates the amount of data which is not correctly identified during classification. The error rate of an algorithm can be evaluated using the below given formula. ...
... Figure 2. Accuracy B. Error rate The error rate of the algorithm demonstrates the amount of data which is not correctly identified during classification. The error rate of an algorithm can be evaluated using the below given formula. ...
Feature Selection, Extraction and Construction
... space using a prespeci ed set of constructive operators. The search starts from an empty set. At each search step, it either adds one possible feature-value pair or deletes one possible feature-value pair in a systematic manner. An evaluation function that takes both class entropy and model complexi ...
... space using a prespeci ed set of constructive operators. The search starts from an empty set. At each search step, it either adds one possible feature-value pair or deletes one possible feature-value pair in a systematic manner. An evaluation function that takes both class entropy and model complexi ...
Detecting Driver Distraction Using a Data Mining Approach
... o Linear regression, decision tree, Support Vector Machines (SVMs), and Bayesian Networks (BNs) have been used to identify various distractions ...
... o Linear regression, decision tree, Support Vector Machines (SVMs), and Bayesian Networks (BNs) have been used to identify various distractions ...
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... (ii) reusing all promising rules discovered from different data sources to form a large set of rules and then searching for valid rules that are useful at the organization level. There are many methods and algorithms suggested for this second task. FP-tree-based frequent patterns mining method was d ...
... (ii) reusing all promising rules discovered from different data sources to form a large set of rules and then searching for valid rules that are useful at the organization level. There are many methods and algorithms suggested for this second task. FP-tree-based frequent patterns mining method was d ...
Density Micro-Clustering Algorithms on Data Streams: A
... on density notion of clusters. They are designed to discover clusters of arbitrary shape and to handle outliers. In fact, in these clustering algorithms the high density area is separated from the low one. Density is defined as the number of points within a specified radius [8]. A density-based clus ...
... on density notion of clusters. They are designed to discover clusters of arbitrary shape and to handle outliers. In fact, in these clustering algorithms the high density area is separated from the low one. Density is defined as the number of points within a specified radius [8]. A density-based clus ...
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.