Supervised Descriptive Rule Discovery: A Unifying Survey of
... count(X) , where count(X,Y ) represents the number of examples for which both X and Y are true, and count(X) represents the number of examples for which X is true. Therefore a more specific contrast set must have higher confidence than any of its generalizations. Further tests for minimum counts and ...
... count(X) , where count(X,Y ) represents the number of examples for which both X and Y are true, and count(X) represents the number of examples for which X is true. Therefore a more specific contrast set must have higher confidence than any of its generalizations. Further tests for minimum counts and ...
transportation data analysis. advances in data mining
... Technological evolution processes which interest different fields, including Information Technology, electronics and telecommunications make easier and less expensive the collection of large amount of data which can be used in transportation analyses. These data include traditional information gather ...
... Technological evolution processes which interest different fields, including Information Technology, electronics and telecommunications make easier and less expensive the collection of large amount of data which can be used in transportation analyses. These data include traditional information gather ...
The interaction between KM and DM is also shown by the current
... Data mining includes several kinds of technologies such as association rule analysis, classification, clustering, sequential pattern etc. In the chapter, we focus on association rule mining since it has been applied in many fields and considered an important method for discovering associations among ...
... Data mining includes several kinds of technologies such as association rule analysis, classification, clustering, sequential pattern etc. In the chapter, we focus on association rule mining since it has been applied in many fields and considered an important method for discovering associations among ...
A Survey on Issues of Decision Tree and Non-Decision
... the leaf is not greater than the sum of the predicted errors for the leaf nodes of that subtree then subtree is replaced with that leaf [18]. 4.7. Minimum Description Length Pruning Mehata et al., and Quinlan and Rivest utilized MDL principle for decision tree pruning [21-22]. The principle of minim ...
... the leaf is not greater than the sum of the predicted errors for the leaf nodes of that subtree then subtree is replaced with that leaf [18]. 4.7. Minimum Description Length Pruning Mehata et al., and Quinlan and Rivest utilized MDL principle for decision tree pruning [21-22]. The principle of minim ...
![ZRL96] Tian Zhang, Raghu Ramakrishnan, and Miron Livny. Birch](http://s1.studyres.com/store/data/000747640_1-addf9ab4ee746db8d51240a091979a0d-300x300.png)
ZRL96] Tian Zhang, Raghu Ramakrishnan, and Miron Livny. Birch
... as to how to treat the missing values in the context of traditional hierarchical clustering. The result of our clustering algorithm with = 0:8 is presented in Table 7. The mutual fund data set is not very amenable to clustering and contains a number of outliers, that is, clusters with only a singl ...
... as to how to treat the missing values in the context of traditional hierarchical clustering. The result of our clustering algorithm with = 0:8 is presented in Table 7. The mutual fund data set is not very amenable to clustering and contains a number of outliers, that is, clusters with only a singl ...
Caching for Multi-dimensional Data Mining Queries
... Researchers have recommended two basic solutions to this problem: precomputation and caching. Both these approaches rely on the ability to answer a query quickly based on the results of prior queries that have been stored by the system. The problem with this is that the space required to store the r ...
... Researchers have recommended two basic solutions to this problem: precomputation and caching. Both these approaches rely on the ability to answer a query quickly based on the results of prior queries that have been stored by the system. The problem with this is that the space required to store the r ...
Random Prism: a noise-tolerant alternative to Random Forests
... Like most rule based classifiers and ensemble classifiers, RDF and RF are based on the ‘divide and conquer’ rule induction approach. ‘Divide and conquer’ induces classification rules in the intermediate form of a decision tree [21, 22]. A more general approach to inducing classification rules is the ...
... Like most rule based classifiers and ensemble classifiers, RDF and RF are based on the ‘divide and conquer’ rule induction approach. ‘Divide and conquer’ induces classification rules in the intermediate form of a decision tree [21, 22]. A more general approach to inducing classification rules is the ...
A novel algorithm for fast and scalable subspace clustering of high
... the 1-dimensional subspaces, are chosen as candidates to be combined together iteratively for computing the higher dimensional clusters. As in Fig. 1, the 1-dimensional clusters from subspaces ({1}, {3} and {4}) are combined to find the clusters in the 2-dimensional subspaces ({1, 3}, {3, 4} and {1, ...
... the 1-dimensional subspaces, are chosen as candidates to be combined together iteratively for computing the higher dimensional clusters. As in Fig. 1, the 1-dimensional clusters from subspaces ({1}, {3} and {4}) are combined to find the clusters in the 2-dimensional subspaces ({1, 3}, {3, 4} and {1, ...
PPT
... identify the objects in low probability regions of the model as outliers Methods are divided into two categories: parametric vs. non-parametric Parametric method Assumes that the normal data is generated by a parametric distribution with parameter θ The probability density function of the parame ...
... identify the objects in low probability regions of the model as outliers Methods are divided into two categories: parametric vs. non-parametric Parametric method Assumes that the normal data is generated by a parametric distribution with parameter θ The probability density function of the parame ...
Unsupervised Domain Adaptation using Parallel Transport on Grassmann Manifold
... as shown in Figures 1 and 2. Figure 1 shows examples of keyboard and backpack images in different domains. While backpack images show changes in shape and texture, keyboard images have variations in viewpoints, but not in texture. In Figure 2, we adapt a dataset of hand-written digits to computer ge ...
... as shown in Figures 1 and 2. Figure 1 shows examples of keyboard and backpack images in different domains. While backpack images show changes in shape and texture, keyboard images have variations in viewpoints, but not in texture. In Figure 2, we adapt a dataset of hand-written digits to computer ge ...
Full-Text
... features in the data that characterize the desired output. They look for clusters of like data. These types of NNs are often called self-organizing neural networks. There are two basics of unsupervised learning: noncompetitive and competitive [2]. Nearest Neighbor Algorithm: An algorithm similar to ...
... features in the data that characterize the desired output. They look for clusters of like data. These types of NNs are often called self-organizing neural networks. There are two basics of unsupervised learning: noncompetitive and competitive [2]. Nearest Neighbor Algorithm: An algorithm similar to ...
thesis full 1 to 6 - Kwame Nkrumah University of Science and
... Due to the wide availability of the web and the internet, ever increasing processing power and the continuous decline in the cost of storage devices, data are being generated massively today than it were decades ago. With fields like the banking industry, data are being generated massively on regula ...
... Due to the wide availability of the web and the internet, ever increasing processing power and the continuous decline in the cost of storage devices, data are being generated massively today than it were decades ago. With fields like the banking industry, data are being generated massively on regula ...
K-nearest neighbors algorithm
In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors.k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms.Both for classification and regression, it can be useful to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor.The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required.A shortcoming of the k-NN algorithm is that it is sensitive to the local structure of the data. The algorithm has nothing to do with and is not to be confused with k-means, another popular machine learning technique.