On the Class Imbalance Problem - Soft Computing and Intelligent
... separately when cleaning the data sets. NCL uses ENN to remove majority examples, for each example Ei in the training set, its three nearest neighbors are found. If Ei belongs to the majority class and the classification given by its three nearest neighbors contradicts the original class of Ei, then ...
... separately when cleaning the data sets. NCL uses ENN to remove majority examples, for each example Ei in the training set, its three nearest neighbors are found. If Ei belongs to the majority class and the classification given by its three nearest neighbors contradicts the original class of Ei, then ...
03_dcluster_jan31_2am
... We define the Hawaiian density of x to be the weighted summation of the tuple-P-tree root counts of the Hawaiian rings, R(x, r-1, r) , which is calculated as follows m ...
... We define the Hawaiian density of x to be the weighted summation of the tuple-P-tree root counts of the Hawaiian rings, R(x, r-1, r) , which is calculated as follows m ...
Uncertain Data Classification Using Decision Tree
... for all the modelled data values of the numerical attributes of the training data sets using probability density function with equal probabilities. For each value of each numerical attribute, an interval is constructed and within the interval a set of ‘n’ sample values are generated using probabilit ...
... for all the modelled data values of the numerical attributes of the training data sets using probability density function with equal probabilities. For each value of each numerical attribute, an interval is constructed and within the interval a set of ‘n’ sample values are generated using probabilit ...
Subspace Clustering and Temporal Mining for Wind
... rule is valid in a set of basic time interval such as {1999, 2, 3}, {2000,2,3}, {2001,2,3}, means that the rule < ( A ∧ B ) → cluster1 > is established during from 1999 to 2001. The wind power data is a very huge data automatically obtained from sensors in a given time interval. So FP-tree structure ...
... rule is valid in a set of basic time interval such as {1999, 2, 3}, {2000,2,3}, {2001,2,3}, means that the rule < ( A ∧ B ) → cluster1 > is established during from 1999 to 2001. The wind power data is a very huge data automatically obtained from sensors in a given time interval. So FP-tree structure ...
a novel approach to construct decision tree using quick c4
... a decision node - specifies some test to be carried out on a single attribute-value, with one branch and sub-tree for each possible outcome of the test. A decision tree can be used to classify an example by starting at the root of the tree and moving through it until a leaf node, which provides the ...
... a decision node - specifies some test to be carried out on a single attribute-value, with one branch and sub-tree for each possible outcome of the test. A decision tree can be used to classify an example by starting at the root of the tree and moving through it until a leaf node, which provides the ...
Energy saving in smart homes based on
... Due to the limitation listed above, in this work, an unsupervised approach for pattern mining is used. In the unsupervised approach, the user is not required to scan his data for activities and the classifier is able to find the patterns autonomously. III. ...
... Due to the limitation listed above, in this work, an unsupervised approach for pattern mining is used. In the unsupervised approach, the user is not required to scan his data for activities and the classifier is able to find the patterns autonomously. III. ...
Data Mining Originally, data mining was a statistician`s term for
... • The connection between diapers and beer. From the use of data mining it was observed that customers who buy diapers are more likely to by beer than average. Supermarkets then placed beer and diapers nearby, knowing many customers would walk between them. Placing potato chips between diapers and be ...
... • The connection between diapers and beer. From the use of data mining it was observed that customers who buy diapers are more likely to by beer than average. Supermarkets then placed beer and diapers nearby, knowing many customers would walk between them. Placing potato chips between diapers and be ...
paper
... the specific parameters of the corresponding distribution, the number of expected outliers, and the space where to expect an outlier. Problems of these classical approaches are obviously the required assumption of a specific distribution in order to apply a specific test. Furthermore, all tests are uni ...
... the specific parameters of the corresponding distribution, the number of expected outliers, and the space where to expect an outlier. Problems of these classical approaches are obviously the required assumption of a specific distribution in order to apply a specific test. Furthermore, all tests are uni ...
No Slide Title
... documents and no other (ideal answer set) Querying process as a process of specifying the properties of an ideal answer set. Since these properties are not known at query time, an initial guess is made This initial guess allows the generation of a preliminary probabilistic description of the ideal a ...
... documents and no other (ideal answer set) Querying process as a process of specifying the properties of an ideal answer set. Since these properties are not known at query time, an initial guess is made This initial guess allows the generation of a preliminary probabilistic description of the ideal a ...
A Dynamic Method for Discovering Density Varied Clusters
... They are closer to density-based algorithms, in that they grow particular clusters so that the preconceived model is improved. However, they sometimes start with a fixed number of clusters and they do not use the same concept of density. Most popular model-based clustering methods are EM [20]. Fuzzy ...
... They are closer to density-based algorithms, in that they grow particular clusters so that the preconceived model is improved. However, they sometimes start with a fixed number of clusters and they do not use the same concept of density. Most popular model-based clustering methods are EM [20]. Fuzzy ...
The k-means clustering technique
... sample mean and covariance) cannot be used. Namely, in exploratory data analysis, one of the assumptions that is made is that no prior knowledge about the dataset, and therefore the dataset’s distribution, is available. In such a situation, data clustering can be a valuable tool. Data clustering is ...
... sample mean and covariance) cannot be used. Namely, in exploratory data analysis, one of the assumptions that is made is that no prior knowledge about the dataset, and therefore the dataset’s distribution, is available. In such a situation, data clustering can be a valuable tool. Data clustering is ...
intro_to_ai. ppt
... Build special-purpose AI systems Determine appropriate dosage for a drug Classify cells as benign or cancerous ...
... Build special-purpose AI systems Determine appropriate dosage for a drug Classify cells as benign or cancerous ...
Cell Probe Lower Bounds for Succinct Data Structures
... How much space do we need to answer these queries? As an example, think of range min. queries (RMinQ): If we return the value of min, then we must store the array. Why? ...
... How much space do we need to answer these queries? As an example, think of range min. queries (RMinQ): If we return the value of min, then we must store the array. Why? ...
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.