GF-DBSCAN: A New Efficient and Effective Data Clustering
... intersects Cluster 1. If the overlapping objects include the core object, then Clusters 1 and 4 should be merged. Likewise, if the new cluster intersects with many clusters, then these clusters are merged into the previous cluster. In Figs 6(d)-6(f), Cluster 5 is the new cluster, and intersects Clus ...
... intersects Cluster 1. If the overlapping objects include the core object, then Clusters 1 and 4 should be merged. Likewise, if the new cluster intersects with many clusters, then these clusters are merged into the previous cluster. In Figs 6(d)-6(f), Cluster 5 is the new cluster, and intersects Clus ...
Dimension Reduction of Chemical Process Simulation Data
... contains n = 33 values for various chemical components, temperature, pressure, and two velocities. The function F (x) is the enthalpy. The associated vector y ∈ Rm has m = 2 and defines the point in the plane to which the values of x and F (x) apply. Problem of that size are easily handled by REDSUB ...
... contains n = 33 values for various chemical components, temperature, pressure, and two velocities. The function F (x) is the enthalpy. The associated vector y ∈ Rm has m = 2 and defines the point in the plane to which the values of x and F (x) apply. Problem of that size are easily handled by REDSUB ...
The Foundations of Cost-Sensitive Learning
... In this section we turn to the question of how to obtain a classifier that is useful for cost-sensitive decision-making. Standard learning algorithms are designed to yield classifiers that maximize accuracy. In the two-class case, these classifiers implicitly make decisions based on the probability ...
... In this section we turn to the question of how to obtain a classifier that is useful for cost-sensitive decision-making. Standard learning algorithms are designed to yield classifiers that maximize accuracy. In the two-class case, these classifiers implicitly make decisions based on the probability ...
Document
... members of the opposite sex with a unique number between 1 and n in order of preference, marry the men and women off such that there are no two people of opposite sex who would both rather have each other than their current partners. If there are no such people, all the marriages are "stable". ...
... members of the opposite sex with a unique number between 1 and n in order of preference, marry the men and women off such that there are no two people of opposite sex who would both rather have each other than their current partners. If there are no such people, all the marriages are "stable". ...
Market Basket Analysis
... percentage of all transactions • Frequent itemset : If an itemset satisfies minimum support,then it is a frequent itemset. • Strong Association rules: Rules that satisfy both a minimum support threshold and a minimum confidence threshold • In Association rule mining, we first find all frequent items ...
... percentage of all transactions • Frequent itemset : If an itemset satisfies minimum support,then it is a frequent itemset. • Strong Association rules: Rules that satisfy both a minimum support threshold and a minimum confidence threshold • In Association rule mining, we first find all frequent items ...
An Efficient Density based Improved K
... often not known in advance when dealing with large databases. (2) Discovery of clusters with arbitrary shape, because the shape of clusters in spatial databases may be spherical, drawnout, linear, elongated etc. (3) Good efficiency on large databases, i.e. on databases of significantly more than jus ...
... often not known in advance when dealing with large databases. (2) Discovery of clusters with arbitrary shape, because the shape of clusters in spatial databases may be spherical, drawnout, linear, elongated etc. (3) Good efficiency on large databases, i.e. on databases of significantly more than jus ...
13 - classes.cs.uchicago.edu
... based on contribution to performance – Need to determine how MUCH change to make ...
... based on contribution to performance – Need to determine how MUCH change to make ...
DM-Lecture-04-05
... Handling Redundant Data in Data Integration Redundant data occur often when integration of ...
... Handling Redundant Data in Data Integration Redundant data occur often when integration of ...
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.