Supervised Local Pattern Discovery
... Supervised local pattern discovery is a midfield task between associations rule mining and inductive learning. It aims at finding patterns in labeled data that are descriptive. Lavrač et al. (2005) describe a pattern as being local while the global counterpart to a pattern is a model, which explains ...
... Supervised local pattern discovery is a midfield task between associations rule mining and inductive learning. It aims at finding patterns in labeled data that are descriptive. Lavrač et al. (2005) describe a pattern as being local while the global counterpart to a pattern is a model, which explains ...
Time Series Contextual Anomaly Detection for Detecting Stock
... In this thesis, we focus on contextual/local anomaly detection within a group of similar time series. The context is defined both in terms of similarity to the neighbourhood data points of each time series and similarity of time series pattern with respect to the rest of time series in the group. Lo ...
... In this thesis, we focus on contextual/local anomaly detection within a group of similar time series. The context is defined both in terms of similarity to the neighbourhood data points of each time series and similarity of time series pattern with respect to the rest of time series in the group. Lo ...
TESI FINALE DI DOTTORATO Mining Informative Patterns in Large
... method that exploits the properties of the user defined constraints and materialized results of past data mining queries to reduce the response times of new queries. Typically, when the dataset is huge, the number of patterns generated can be very large and unmanageable for a human user, and only a ...
... method that exploits the properties of the user defined constraints and materialized results of past data mining queries to reduce the response times of new queries. Typically, when the dataset is huge, the number of patterns generated can be very large and unmanageable for a human user, and only a ...
Comparison of Chi-Square Based Algorithms for Discretization of
... Data mining is the collection of numerous methods and techniques to reveal meaningful patterns, valid and useful information in massive volumes of data. In many data mining applications such as feature selection, classification and association rules extraction, the majority of the algorithms have pr ...
... Data mining is the collection of numerous methods and techniques to reveal meaningful patterns, valid and useful information in massive volumes of data. In many data mining applications such as feature selection, classification and association rules extraction, the majority of the algorithms have pr ...
Classification
... model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it. ...
... model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it. ...
Efficient Clustering of High-Dimensional Data Sets
... A canopy is simply a subset of the elements (i.e. data points or items) that, according to the approximate similarity measure, are within some distance threshold from a central point. Significantly, an element may appear under more than one canopy, and every element must appear in at least one canop ...
... A canopy is simply a subset of the elements (i.e. data points or items) that, according to the approximate similarity measure, are within some distance threshold from a central point. Significantly, an element may appear under more than one canopy, and every element must appear in at least one canop ...
Data Mining of Range-Based Classification Rules for Data
... more specifically continuous, data attributes. In real world applications this is expected to be the case in the majority of tasks since real world data collections often contain real numbers. However existing work in the area of classification rule mining has focused primarily on mining data of a c ...
... more specifically continuous, data attributes. In real world applications this is expected to be the case in the majority of tasks since real world data collections often contain real numbers. However existing work in the area of classification rule mining has focused primarily on mining data of a c ...
Studies in Classification, Data Analysis, and Knowledge Organization
... an ontology restricted to subsumption links. We outline some limitations of these measures and introduce a new one: the Proportion of Shared Specificity. This measure which does not depend on an external corpus, takes into account the density of links in the graph between two concepts. A numerical co ...
... an ontology restricted to subsumption links. We outline some limitations of these measures and introduce a new one: the Proportion of Shared Specificity. This measure which does not depend on an external corpus, takes into account the density of links in the graph between two concepts. A numerical co ...
Outlier Detection Techniques
... – Given a smoothing factor SF(I) that computes for each I DB how much the variance of DB is decreased when I is removed from DB – If two sets have an equal SF value, take the smaller set – The outliers are the elements of the exception set E DB for which the following holds: SF(E) SF(I) for al ...
... – Given a smoothing factor SF(I) that computes for each I DB how much the variance of DB is decreased when I is removed from DB – If two sets have an equal SF value, take the smaller set – The outliers are the elements of the exception set E DB for which the following holds: SF(E) SF(I) for al ...
Review on Clustering in Data Mining
... In hierarchical clustering our regular point-by-attribute data representation is sometimes of secondary importance. Instead, hierarchical clustering frequently deals with the NxN matrix of distances (dissimilarities) or similarities between training points. It is sometimes called connectivity matrix ...
... In hierarchical clustering our regular point-by-attribute data representation is sometimes of secondary importance. Instead, hierarchical clustering frequently deals with the NxN matrix of distances (dissimilarities) or similarities between training points. It is sometimes called connectivity matrix ...
1 META MINING SYSTEM FOR SUPERVISED LEARNING by
... Supervised inductive machine learning is one of several powerful methodologies that can be used for performing a Data Mining task. Data Mining aims to find previously unknown, implicit patterns that exist in large data sets, but are hidden among large quantities of data. These patterns describe pote ...
... Supervised inductive machine learning is one of several powerful methodologies that can be used for performing a Data Mining task. Data Mining aims to find previously unknown, implicit patterns that exist in large data sets, but are hidden among large quantities of data. These patterns describe pote ...
T._Ravindra_Ba .V._Subrah(BookZZ.org)
... Domain knowledge forms an important input for efficient compaction. Such knowledge could either be provided by a human expert or generated through an appropriate preliminary statistical analysis. In Chap. 6, we exploit domain knowledge obtained both by expert inference and through statistical analys ...
... Domain knowledge forms an important input for efficient compaction. Such knowledge could either be provided by a human expert or generated through an appropriate preliminary statistical analysis. In Chap. 6, we exploit domain knowledge obtained both by expert inference and through statistical analys ...
Customer Activity Sequence Classification for Debt Prevention in
... in sequence classification, it is much more important to discover discriminative patterns than a complete pattern set. In this paper, we propose a novel hierarchical algorithm to build sequential classifiers using discriminative sequential patterns. Firstly, we mine for the sequential patterns which a ...
... in sequence classification, it is much more important to discover discriminative patterns than a complete pattern set. In this paper, we propose a novel hierarchical algorithm to build sequential classifiers using discriminative sequential patterns. Firstly, we mine for the sequential patterns which a ...
tdp.a020a09
... In general, the solution to prevent linkage attacks in de-identified data sets is anonymization [42, 41], and k-anonymity was proposed as a standard for privacy over relational databases. We can summarize k-Anonymity as “safety in numbers” which ensures that every entity in the table is indistinguis ...
... In general, the solution to prevent linkage attacks in de-identified data sets is anonymization [42, 41], and k-anonymity was proposed as a standard for privacy over relational databases. We can summarize k-Anonymity as “safety in numbers” which ensures that every entity in the table is indistinguis ...
Outlier Detection Techniques
... Discussion: – Similar idea like classical statistical approaches (k = 1 distributions) but independent p from the chosen kind of distribution – Naïve solution is in O(2n) for n data objects – Heuristics like random sampling or best first search are applied – Applicable to any data type (depends on t ...
... Discussion: – Similar idea like classical statistical approaches (k = 1 distributions) but independent p from the chosen kind of distribution – Naïve solution is in O(2n) for n data objects – Heuristics like random sampling or best first search are applied – Applicable to any data type (depends on t ...
Multinomial Logistic Regression
... Multinomial logistic regression is used to predict categorical placement in or the probability of category membership on a dependent variable based on multiple independent variables. The independent variables can be either dichotomous (i.e., binary) or continuous (i.e., interval or ratio in scale). ...
... Multinomial logistic regression is used to predict categorical placement in or the probability of category membership on a dependent variable based on multiple independent variables. The independent variables can be either dichotomous (i.e., binary) or continuous (i.e., interval or ratio in scale). ...
chap4_basic_classifi..
... model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it. ...
... model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it. ...
1 - Supporting Advancement
... model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it. ...
... model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it. ...
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.