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 ...
S4904131136
... principle that the instances within a dataset will generally exist in close proximity to other instances that have similar properties. As kNN does not make any assumptions on the underlying data distribution and does not use the training data points to do any generalization, it is called as non para ...
... principle that the instances within a dataset will generally exist in close proximity to other instances that have similar properties. As kNN does not make any assumptions on the underlying data distribution and does not use the training data points to do any generalization, it is called as non para ...
Spatio-temporal clustering
... Another approach to cluster complex form of data, like trajectories, is to transform the complex objects into features vectors, i.e. a set of multidimensional vectors where each dimension represents a single characteristic of the original object, and then to cluster them using generic clustering alg ...
... Another approach to cluster complex form of data, like trajectories, is to transform the complex objects into features vectors, i.e. a set of multidimensional vectors where each dimension represents a single characteristic of the original object, and then to cluster them using generic clustering alg ...
Graph-Based Structures for the Market Baskets Analysis
... Given two item-clientele A and B, the first two similarity functions that come up are the number of matches and the hamming distance. The number of matches is given by the cardinality of (A∩B), while on the other hand, the hamming distance is given by the sum of the cardinalities of the sets (A-B) a ...
... Given two item-clientele A and B, the first two similarity functions that come up are the number of matches and the hamming distance. The number of matches is given by the cardinality of (A∩B), while on the other hand, the hamming distance is given by the sum of the cardinalities of the sets (A-B) a ...
Multi-Agent Clustering - Computer Science Intranet
... The operation of the proposed MADM clustering mechanism is described in this section. As noted in the foregoing, Clustering Agents are spawned by a User Agent according to the nature of the end user’s initial “clustering request”. Fundamentally there are two strategies for spawning Clustering Agents ...
... The operation of the proposed MADM clustering mechanism is described in this section. As noted in the foregoing, Clustering Agents are spawned by a User Agent according to the nature of the end user’s initial “clustering request”. Fundamentally there are two strategies for spawning Clustering Agents ...
Graph-based and Lexical-Syntactic Approaches for the Authorship Attribution Task
... By employing the kernel function, it is not necessary to explicitly calculate the mapping φ : X → F in order to learn in the feature space. In this research work, we employed as kernel the polynomial mapping, which is a very popular method for modeling non-linear functions: K(x, x) = (hx, xi + c)d ...
... By employing the kernel function, it is not necessary to explicitly calculate the mapping φ : X → F in order to learn in the feature space. In this research work, we employed as kernel the polynomial mapping, which is a very popular method for modeling non-linear functions: K(x, x) = (hx, xi + c)d ...
Association Rule Mining Using Firefly Algorithm
... Data mining can be classified into several techniques, including association rules, clustering and classification, time series analysis and sequence discovery. Among these techniques, association rule mining is the most widely significant method for extracting useful and hidden information from larg ...
... Data mining can be classified into several techniques, including association rules, clustering and classification, time series analysis and sequence discovery. Among these techniques, association rule mining is the most widely significant method for extracting useful and hidden information from larg ...
Machine Learning Challenges: Choosing the Best Model
... model takes a vote to see where it should be classed. If you’re performing a regression problem and want to find a continuous number, take the mean of f values of k nearest neighbors. Although the training time of kNN is short, actual query time (and storage space) might be longer than that of other ...
... model takes a vote to see where it should be classed. If you’re performing a regression problem and want to find a continuous number, take the mean of f values of k nearest neighbors. Although the training time of kNN is short, actual query time (and storage space) might be longer than that of other ...
An Improved Frequent Itemset Generation Algorithm Based On
... Since the algorithm is based on the array index mapping, the algorithm is best suitable when used for the incremental approach, i.e. as and when the data is entered into the database, the value of the particular array index is incremented corresponding to the items. Hence it is not required to expli ...
... Since the algorithm is based on the array index mapping, the algorithm is best suitable when used for the incremental approach, i.e. as and when the data is entered into the database, the value of the particular array index is incremented corresponding to the items. Hence it is not required to expli ...
Comparative Analysis of Bayes and Lazy Classification
... The K* algorithm can be defined as a method of cluster analysis which mainly aims at the partition of „n‟ observation into „k‟ clusters in which each observation belongs to the cluster with the nearest mean. We can describe K* algorithm as an instance based learner which uses entropy as a distance m ...
... The K* algorithm can be defined as a method of cluster analysis which mainly aims at the partition of „n‟ observation into „k‟ clusters in which each observation belongs to the cluster with the nearest mean. We can describe K* algorithm as an instance based learner which uses entropy as a distance m ...
ppt
... clustered Insert points one at a time into the R-tree, merging a new point with an existing cluster if is less than some distance away If there are more leaf nodes than fit in memory, merge existing clusters that are close to each other At the end of first pass we get a large number of clust ...
... clustered Insert points one at a time into the R-tree, merging a new point with an existing cluster if is less than some distance away If there are more leaf nodes than fit in memory, merge existing clusters that are close to each other At the end of first pass we get a large number of clust ...
Discovering Overlapping Quantitative Associations by
... high density (similar to the notion of high frequency for traditional itemset mining). Please note that this step might be considered as a sort of discretization as we have to fix intervals at some point. However, it is by far more flexible than pre-discretization as it allows for on-the-fly genera ...
... high density (similar to the notion of high frequency for traditional itemset mining). Please note that this step might be considered as a sort of discretization as we have to fix intervals at some point. However, it is by far more flexible than pre-discretization as it allows for on-the-fly genera ...
Presenting a Novel Method for Mining Association Rules Using
... non-useful; therefore, it can be said that these algorithms are less efficient in large databases [6]. Thus, there is a need for a method which can discover efficient and optimal rules in large databases so that managers can make more effective decisions using these optimal rules. Genetic algorithm ...
... non-useful; therefore, it can be said that these algorithms are less efficient in large databases [6]. Thus, there is a need for a method which can discover efficient and optimal rules in large databases so that managers can make more effective decisions using these optimal rules. Genetic algorithm ...
Similarity-based clustering of sequences using hidden Markov models
... standard pairwise distance matrix-based approaches (as agglomerative hierarchical) were then used to obtain clustering. This strategy, which is considered the standard method for HMM-based clustering of sequences, is better detailed in the Section 3.1. The first approach not directly linked to speec ...
... standard pairwise distance matrix-based approaches (as agglomerative hierarchical) were then used to obtain clustering. This strategy, which is considered the standard method for HMM-based clustering of sequences, is better detailed in the Section 3.1. The first approach not directly linked to speec ...
A Systematic Review of Classification Techniques and
... connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase [1]. After the training is complete the parameter are fixed. If there are lots of data and p ...
... connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase [1]. After the training is complete the parameter are fixed. If there are lots of data and p ...
Document
... Expectation Maximization algorithm Select an initial set of model parameters Repeat Expectation Step: For each object, calculate the probability that it belongs to each distribution i, i.e., prob(xi|i) Maximization Step: Given the probabilities from the expectation step, find the new estimates of ...
... Expectation Maximization algorithm Select an initial set of model parameters Repeat Expectation Step: For each object, calculate the probability that it belongs to each distribution i, i.e., prob(xi|i) Maximization Step: Given the probabilities from the expectation step, find the new estimates of ...
On the use of Side Information for Mining Text Data
... corpus S of text documents. The total number of documents is N , and they are denoted by T1 . . . TN . It is assumed that the set of distinct words in the entire corpus S is denoted by W. Associated with each document Ti , we have a set of side attributes Xi . Each set of side attributes Xi has d di ...
... corpus S of text documents. The total number of documents is N , and they are denoted by T1 . . . TN . It is assumed that the set of distinct words in the entire corpus S is denoted by W. Associated with each document Ti , we have a set of side attributes Xi . Each set of side attributes Xi has d di ...
Top 10 Algorithms in Data Mining
... September 2006 to each nominate up to 10 best-known algorithms Each nomination was asked to come with the following information: (a) the algorithm name, (b) a brief justification, and (c) a representative publication reference Each nominated algorithm should have been widely cited and used by ot ...
... September 2006 to each nominate up to 10 best-known algorithms Each nomination was asked to come with the following information: (a) the algorithm name, (b) a brief justification, and (c) a representative publication reference Each nominated algorithm should have been widely cited and used by ot ...