Unsupervised Identification of the User’s Query Intent in Web Search Liliana Calderón-Benavides
... and all the members of the WRG and Yahoo! Research Barcelona. I would like to thank Vicente Lopez and Joan Codina from Barcelona Media, I learnt very much from our work together. I thank Devdatt Dubhashi for his invitation to work together at Chalmers University of Technology / Göteborg University, ...
... and all the members of the WRG and Yahoo! Research Barcelona. I would like to thank Vicente Lopez and Joan Codina from Barcelona Media, I learnt very much from our work together. I thank Devdatt Dubhashi for his invitation to work together at Chalmers University of Technology / Göteborg University, ...
Abnormal Pattern Recognition in Spatial Data
... from both industry and academia, and has become an important branch of data mining. Abnormal spatial patterns, or spatial outliers, are those observations whose characteristics are markedly different from their spatial neighbors. The identification of spatial outliers can be used to reveal hidden bu ...
... from both industry and academia, and has become an important branch of data mining. Abnormal spatial patterns, or spatial outliers, are those observations whose characteristics are markedly different from their spatial neighbors. The identification of spatial outliers can be used to reveal hidden bu ...
A Detailed Introduction to K-Nearest Neighbor (KNN) Algorithm
... The data can be scalars or possibly even multidimensional vectors. Since the points are in feature space, they have a notion of distance – This need not necessarily be Euclidean distance although it is the one commonly used. Each of the training data consists of a set of vectors and class label asso ...
... The data can be scalars or possibly even multidimensional vectors. Since the points are in feature space, they have a notion of distance – This need not necessarily be Euclidean distance although it is the one commonly used. Each of the training data consists of a set of vectors and class label asso ...
MODEL-BASED OUTLIER DETECTION FOR OBJECT
... of firmly labelling it as either outlier or normal. • Designing an outlier detection method depends on the type of application: one of the earliest steps in designing a model to identify outliers is choosing a similarity or distance measure. However, different applications require different sensibil ...
... of firmly labelling it as either outlier or normal. • Designing an outlier detection method depends on the type of application: one of the earliest steps in designing a model to identify outliers is choosing a similarity or distance measure. However, different applications require different sensibil ...
Mining with Rarity: A Unifying Framework
... regularities can then only be found within each individual partition, which will contain less data. While data fragmentation is always a concern, it is more of a concern when mining rare classes/cases, because of the existing “lack of data” problem described in Section 2.2. Thus all iterative divide ...
... regularities can then only be found within each individual partition, which will contain less data. While data fragmentation is always a concern, it is more of a concern when mining rare classes/cases, because of the existing “lack of data” problem described in Section 2.2. Thus all iterative divide ...
A Survey on Trajectory Data Mining
... dimensions). Trajectory data are generated by various moving objects and collected from multiple data sources [68]. A trajectory T is defined as an ordered list of spatiotemporal sample points p1, p2, p3, …, pn where each pi = (xi, yi, ti) and xi, yi, are the spatial coordinates of the sampled point ...
... dimensions). Trajectory data are generated by various moving objects and collected from multiple data sources [68]. A trajectory T is defined as an ordered list of spatiotemporal sample points p1, p2, p3, …, pn where each pi = (xi, yi, ti) and xi, yi, are the spatial coordinates of the sampled point ...
Data Mining of Range-Based Classification Rules for Data
... Another important distinction between data mining tasks is based on the type of data mined. The type of each attribute is indicative of its underlying properties and therefore an important aspect of a data mining method is the type of data it is designed to mine. The iris data used in the example co ...
... Another important distinction between data mining tasks is based on the type of data mined. The type of each attribute is indicative of its underlying properties and therefore an important aspect of a data mining method is the type of data it is designed to mine. The iris data used in the example co ...
Contents
... Imagine that you are a manager at AllElectronics and have been charged with analyzing the company’s data with respect to the sales at your branch. You immediately set out to perform this task. You carefully inspect the company’s database and data warehouse, identifying and selecting the attributes o ...
... Imagine that you are a manager at AllElectronics and have been charged with analyzing the company’s data with respect to the sales at your branch. You immediately set out to perform this task. You carefully inspect the company’s database and data warehouse, identifying and selecting the attributes o ...
Multimedia Engineering
... from data to construct an ontology. It can also be used to enrich existing ontology. Traditional clustering techniques are useful for generating non-taxonomy relations for ontology. In particular, conceptual clustering techniques are powerful clustering techniques that can conceptualize clusters and ...
... from data to construct an ontology. It can also be used to enrich existing ontology. Traditional clustering techniques are useful for generating non-taxonomy relations for ontology. In particular, conceptual clustering techniques are powerful clustering techniques that can conceptualize clusters and ...
Data Transformation For Privacy
... In this thesis, we focus primarily on privacy issues in data mining, notably when data are shared before mining. Specifically, we consider some scenarios in which applications of association rule mining and data clustering require privacy safeguards. Addressing privacy preservation in such scenarios ...
... In this thesis, we focus primarily on privacy issues in data mining, notably when data are shared before mining. Specifically, we consider some scenarios in which applications of association rule mining and data clustering require privacy safeguards. Addressing privacy preservation in such scenarios ...
K - Department of Computer Science
... Paul Horton and Kenta Nakai. Better prediction of protein cellular localization sites with the k nearest neighbors classifier. In Proceeding of the Fifth International Conference on Intelligent Systems for Molecular Biology, pages 147-152, Menlo Park, 1997. AAAI Press. J.M. Keller, M.R. Gray, and jr ...
... Paul Horton and Kenta Nakai. Better prediction of protein cellular localization sites with the k nearest neighbors classifier. In Proceeding of the Fifth International Conference on Intelligent Systems for Molecular Biology, pages 147-152, Menlo Park, 1997. AAAI Press. J.M. Keller, M.R. Gray, and jr ...
Locally linear embedding algorithm. Extensions and applications
... been developed with an aim to reduce or eliminate information bearing secondary importance, and retain or highlight meaningful information while reducing the dimensionality of data. Since the nature of real-world data is often nonlinear, linear dimensionality reduction techniques, such as principal ...
... been developed with an aim to reduce or eliminate information bearing secondary importance, and retain or highlight meaningful information while reducing the dimensionality of data. Since the nature of real-world data is often nonlinear, linear dimensionality reduction techniques, such as principal ...
Document
... sensitive to some “distortions” in the data. For most problems these distortions are not meaningful, and thus we can and should remove them In the next few slides we will discuss the 4 most common distortions, and how to remove them (c) Eamonn Keogh, [email protected] ...
... sensitive to some “distortions” in the data. For most problems these distortions are not meaningful, and thus we can and should remove them In the next few slides we will discuss the 4 most common distortions, and how to remove them (c) Eamonn Keogh, [email protected] ...
Rule extraction using Recursive-Rule extraction algorithm with
... models. A drawback of black-box models is that they cannot adequately reveal information that may be hidden in the data. For example, even in cases for which high-performance classifiers [2,4,8,24,25,32,33] allow the accurate assignment of instances to groups, black-box models are unable to provide t ...
... models. A drawback of black-box models is that they cannot adequately reveal information that may be hidden in the data. For example, even in cases for which high-performance classifiers [2,4,8,24,25,32,33] allow the accurate assignment of instances to groups, black-box models are unable to provide t ...
Lecture 06 Multimedia Data Mining and Knowledge Discovery
... relationships, i.e., “association rules,” among variables in large databases used for data mining; Classification = a set of techniques to identify the categories in which new data points belong, based on a training set containing data points that have already been categorized; these techniques ...
... relationships, i.e., “association rules,” among variables in large databases used for data mining; Classification = a set of techniques to identify the categories in which new data points belong, based on a training set containing data points that have already been categorized; these techniques ...
Cluster analysis
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It will often be necessary to modify data preprocessing and model parameters until the result achieves the desired properties.Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek βότρυς ""grape"") and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals.Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 and Robert Tryon in 1939 and famously used by Cattell beginning in 1943 for trait theory classification in personality psychology.