PPT - University of Maryland at College Park
... Establishes all possible values by listing them Supports values(), valueOf(), name(), compareTo()… Can add fields and methods to enums Example public enum Color { Black, White } // new enumeration Color myC = Color.Black; for (Color c : Color.values()) System.out.println(c); When to use enums Natura ...
... Establishes all possible values by listing them Supports values(), valueOf(), name(), compareTo()… Can add fields and methods to enums Example public enum Color { Black, White } // new enumeration Color myC = Color.Black; for (Color c : Color.values()) System.out.println(c); When to use enums Natura ...
System Configuration - Millennium Software Solutions
... PROPOSED SYSTEMS:In these problems, the available information about the users is often not in the form of histograms, and the solutions proposed are often based on heuristics and practical convenience; whereas the solution we propose in this paper is specific to the setting in which the only informa ...
... PROPOSED SYSTEMS:In these problems, the available information about the users is often not in the form of histograms, and the solutions proposed are often based on heuristics and practical convenience; whereas the solution we propose in this paper is specific to the setting in which the only informa ...
Improved Hybrid Clustering and Distance
... clustering operation. The authors of [15] initialized the concept of distance-based outlier, which defines an object 'O' being an outlier, if at most 'p' objects are within the distance 'd' of 'O'. In the distance-based approach, outliers are detected as follows. Given a distance measure on a featur ...
... clustering operation. The authors of [15] initialized the concept of distance-based outlier, which defines an object 'O' being an outlier, if at most 'p' objects are within the distance 'd' of 'O'. In the distance-based approach, outliers are detected as follows. Given a distance measure on a featur ...
KEEL Data-Mining Software Tool: Data Set Repository, Integration of
... • It extends the range of possible users applying evolutionary learning algorithms. An extensive library of EAs together with easy-to-use software considerably reduce the level of knowledge and experience required by researchers in evolutionary computation. As a result researchers with less knowledg ...
... • It extends the range of possible users applying evolutionary learning algorithms. An extensive library of EAs together with easy-to-use software considerably reduce the level of knowledge and experience required by researchers in evolutionary computation. As a result researchers with less knowledg ...
30. An Efficient Index Support for Item Set Mining using
... combine data mining activities with relational DBMSs, but a correct incorporation into the relational DBMS [2] kernel has been infrequently achieved. This paper suggested an innovative indexing method, which denotes the transactions in a succinct form, suitable for tightly incorporating frequent ite ...
... combine data mining activities with relational DBMSs, but a correct incorporation into the relational DBMS [2] kernel has been infrequently achieved. This paper suggested an innovative indexing method, which denotes the transactions in a succinct form, suitable for tightly incorporating frequent ite ...
Conditional Random Fields for Identifying Appropriate Types of
... “should”, large number of strong subjective expressions, and imperatives, which are all highly correlated with the U N V ERIF. While the constraints are accurate, the coverage is too small to boost the performance. However, it is quite difficult to generate a large set of constraints, because there ...
... “should”, large number of strong subjective expressions, and imperatives, which are all highly correlated with the U N V ERIF. While the constraints are accurate, the coverage is too small to boost the performance. However, it is quite difficult to generate a large set of constraints, because there ...
Towards Linked Open Data enabled Data Mining
... for each incoming or outgoing relation of an entity, including the related types, i.e., they are concerned with qualified relations Furthermore, we implemented approaches for generating features based on graph sub-structures using graph kernels: the Weisfeiler-Lehman Kernel [5], and the Intersectio ...
... for each incoming or outgoing relation of an entity, including the related types, i.e., they are concerned with qualified relations Furthermore, we implemented approaches for generating features based on graph sub-structures using graph kernels: the Weisfeiler-Lehman Kernel [5], and the Intersectio ...
Time series feature extraction for data mining using
... often suffer so baldy from many dimensions that a sequential scan of the database will be faster than using the index. But using all time points isn’t really necessary, because time series usually show a lot of redundancy and noise that should be removed. A solution to all the problems mentioned abo ...
... often suffer so baldy from many dimensions that a sequential scan of the database will be faster than using the index. But using all time points isn’t really necessary, because time series usually show a lot of redundancy and noise that should be removed. A solution to all the problems mentioned abo ...
A Survey Report on RFM Pattern Matching Using Efficient
... knowledge for selecting targets for direct marketing from a database. This methodology leads to more efficient and accurate selection procedures than the existing ones. In the empirical part he examined a case study, blood transfusion service, to show that the methodology has greater predictive accu ...
... knowledge for selecting targets for direct marketing from a database. This methodology leads to more efficient and accurate selection procedures than the existing ones. In the empirical part he examined a case study, blood transfusion service, to show that the methodology has greater predictive accu ...
EZ36937941
... 4.2. Decision Trees - Decision trees are one of the most regularly used techniques of data analysis [8]. Decision trees are easy to visualize and understand and resistant to noise in data [12]. Generally, decision trees are used to classify records to a proper class. Besides, they are applicable in ...
... 4.2. Decision Trees - Decision trees are one of the most regularly used techniques of data analysis [8]. Decision trees are easy to visualize and understand and resistant to noise in data [12]. Generally, decision trees are used to classify records to a proper class. Besides, they are applicable in ...
Proceedings of the 21st Australasian Joint Conference on Artificial
... proficient players. Most GGP players have used standard tree-search techniques ... General Game Playing (GGP) aims at developing game playing agents that are able to play a variety of games and, in the absence of pre-programmed game specific knowledge, become proficient players. Most GGP players hav ...
... proficient players. Most GGP players have used standard tree-search techniques ... General Game Playing (GGP) aims at developing game playing agents that are able to play a variety of games and, in the absence of pre-programmed game specific knowledge, become proficient players. Most GGP players hav ...
Data Mining: Concepts and Techniques
... Let H be a hypothesis that X belongs to class C Classification is to determine P(H|X), (posteriori probability), the probability that the hypothesis holds given the observed data sample X P(H) (prior probability), the initial probability E.g., X will buy computer, regardless of age, income, … P(X) ...
... Let H be a hypothesis that X belongs to class C Classification is to determine P(H|X), (posteriori probability), the probability that the hypothesis holds given the observed data sample X P(H) (prior probability), the initial probability E.g., X will buy computer, regardless of age, income, … P(X) ...
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.