Outlier Detection in Online Gambling
... techniques is mostly subjective. A quite common separation is that made by Hand [10], who separates data mining into categories according to the outcome of the tasks they perform. These categories are: 1. Exploratory Data Analysis. Which intents to explore the data without aiming somewhere specifica ...
... techniques is mostly subjective. A quite common separation is that made by Hand [10], who separates data mining into categories according to the outcome of the tasks they perform. These categories are: 1. Exploratory Data Analysis. Which intents to explore the data without aiming somewhere specifica ...
Data Mining Approaches for Intrusion Detection
... such systems are: known intrusion patterns have to be hand-coded into the system; they are unable to detect any future (unknown) intrusions that have no matched patterns stored in the system. Anomaly detection (sub)systems, such as IDES [LTG+ 92], establish normal usage patterns (profiles) using sta ...
... such systems are: known intrusion patterns have to be hand-coded into the system; they are unable to detect any future (unknown) intrusions that have no matched patterns stored in the system. Anomaly detection (sub)systems, such as IDES [LTG+ 92], establish normal usage patterns (profiles) using sta ...
Data Mining for Decision Making in Multi
... Data mining is defined as the process of discovering patterns in data. The process must be automatic or (more usually) semiautomatic. The patterns discovered must be meaningful in that they lead to some advantage. The data is invariably present in substantial quantities. Useful patterns allow us to ...
... Data mining is defined as the process of discovering patterns in data. The process must be automatic or (more usually) semiautomatic. The patterns discovered must be meaningful in that they lead to some advantage. The data is invariably present in substantial quantities. Useful patterns allow us to ...
Discovering Similar Patterns in Time Series
... time series. This has obliged us to design a new algorithm, based on Han’s, to tackle this problem. ...
... time series. This has obliged us to design a new algorithm, based on Han’s, to tackle this problem. ...
Instant Selection of High Contrast Projections in Multi
... Let us start with some basic notions for our formalization. We model a stream database DB as an infinite set of time points DB = {t0 , t1 , t3 , . . .}, with each time point i storing a d-dimensional vector ti ∈ Rd . The full data space is represented by the dimension set DIM = {D1 , . . . , Dd }. A ...
... Let us start with some basic notions for our formalization. We model a stream database DB as an infinite set of time points DB = {t0 , t1 , t3 , . . .}, with each time point i storing a d-dimensional vector ti ∈ Rd . The full data space is represented by the dimension set DIM = {D1 , . . . , Dd }. A ...
K-NEAREST NEIGHBOR BASED DBSCAN CLUSTERING
... DBSCAN algorithm i.e. determination of Epsilon value and Minimum number of points and further proposed a novel efficient DBSCAN algorithm as to overcome this drawback. This proposed approach is introduced mainly for the applications on images as to segment the images very efficiently depending on th ...
... DBSCAN algorithm i.e. determination of Epsilon value and Minimum number of points and further proposed a novel efficient DBSCAN algorithm as to overcome this drawback. This proposed approach is introduced mainly for the applications on images as to segment the images very efficiently depending on th ...
Classification and Prediction - Computer Science
... Randomly select a sub set of features Create a fully grown decision tree on the n data points, with the sub feature set, sampled with replacement. Repeat the two steps to create a large number of trees forming a random forest. Apply each tree in the forest on test data and use majority vote of all t ...
... Randomly select a sub set of features Create a fully grown decision tree on the n data points, with the sub feature set, sampled with replacement. Repeat the two steps to create a large number of trees forming a random forest. Apply each tree in the forest on test data and use majority vote of all t ...
Deep Feature Synthesis: Towards Automating Data
... dfeat features for entities we have visited. This ensures we avoid calculating rfeat features unnecessarily. For example, consider a case where we are making features for the Customer entity in our example e-commerce database. It would not make sense to create a dfeat for each order that pulled in t ...
... dfeat features for entities we have visited. This ensures we avoid calculating rfeat features unnecessarily. For example, consider a case where we are making features for the Customer entity in our example e-commerce database. It would not make sense to create a dfeat for each order that pulled in t ...
Minimum Entropy Clustering and Applications to Gene Expression
... genes in the same cluster are probably involved in the same cellular process and strong expression pattern correlation between those genes indicates co-regulation. The clustering problem can be formally stated as follows: given a dataset of X = {x i |i = 1, . . . , n} and an integer m > 1, map X on ...
... genes in the same cluster are probably involved in the same cellular process and strong expression pattern correlation between those genes indicates co-regulation. The clustering problem can be formally stated as follows: given a dataset of X = {x i |i = 1, . . . , n} and an integer m > 1, map X on ...
Weka: Practical Machine Learning Tools and Techniques with Java
... globally replace them before the learning scheme is applied. ReplaceMissingValuesFilter substitutes the mean (for numeric attributes) or the mode (for nominal attributes) for each missing value. Transforming numeric attributes Some filters pertain specifically to numeric attributes. For example, an ...
... globally replace them before the learning scheme is applied. ReplaceMissingValuesFilter substitutes the mean (for numeric attributes) or the mode (for nominal attributes) for each missing value. Transforming numeric attributes Some filters pertain specifically to numeric attributes. For example, an ...
Neighborhood rough sets for dynamic data mining
... mining.11–18 For instance, a conceptually simple and easy to implement method based on the rough sets was to construct neighborhood-based attribute reduction technique and classifiers.11 A theoretic framework based on rough set theory, named the positive approximation, was to accelerate algorithms o ...
... mining.11–18 For instance, a conceptually simple and easy to implement method based on the rough sets was to construct neighborhood-based attribute reduction technique and classifiers.11 A theoretic framework based on rough set theory, named the positive approximation, was to accelerate algorithms o ...
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.