Data Mining on Empty Result Queries
... Management (CRM) database application developed by IBM, 18.07% (3,396) queries are empty-result ones. The empty-result problem has been studied in the research literature. It is known as empty-result problem in [Kießling and Köstler, 2002]. According to [Luo, 2006], existing solutions fall into two ...
... Management (CRM) database application developed by IBM, 18.07% (3,396) queries are empty-result ones. The empty-result problem has been studied in the research literature. It is known as empty-result problem in [Kießling and Köstler, 2002]. According to [Luo, 2006], existing solutions fall into two ...
DOC Version
... Contrasting specific groups of interest plays a key role in social science research. Bay & Pazzani (2001, p.213) aims to automatically detect all the differences between contrasting groups from observational multivariate data. Based on Bay and Pazzani (2001, p.217), the data is a set of groups G1, G ...
... Contrasting specific groups of interest plays a key role in social science research. Bay & Pazzani (2001, p.213) aims to automatically detect all the differences between contrasting groups from observational multivariate data. Based on Bay and Pazzani (2001, p.217), the data is a set of groups G1, G ...
An adaptive modular approach to the mining of sensor
... m first eigenvectors of x correlation matrix, or Minimization of ...
... m first eigenvectors of x correlation matrix, or Minimization of ...
Contents - Computer Science
... the merge of a set of geographic areas by spatial operations, such as spatial union or spatial clustering methods. Aggregation and approximation are important techniques in such generalization. In spatial merge, it is necessary to not only merge the regions of similar types within the same general c ...
... the merge of a set of geographic areas by spatial operations, such as spatial union or spatial clustering methods. Aggregation and approximation are important techniques in such generalization. In spatial merge, it is necessary to not only merge the regions of similar types within the same general c ...
slides in pdf
... Collocation: A sequence of words that occur more frequently than expected Often “interesting” and due to their non‐compositionality, often relay information not portrayed by their constituent terms (e.g., “made an exception”, “strong tea”) Many different measures used to extract collocations from a ...
... Collocation: A sequence of words that occur more frequently than expected Often “interesting” and due to their non‐compositionality, often relay information not portrayed by their constituent terms (e.g., “made an exception”, “strong tea”) Many different measures used to extract collocations from a ...
Feature Selection: An Ever Evolving Frontier in Data Mining
... set of selected features are good enough using certain stopping criterion. If it is, a feature selection algorithm will return the set of selected features, otherwise, it iterates until the stopping criterion is met. In the process of generating the candidate set and evaluating it, a feature selecti ...
... set of selected features are good enough using certain stopping criterion. If it is, a feature selection algorithm will return the set of selected features, otherwise, it iterates until the stopping criterion is met. In the process of generating the candidate set and evaluating it, a feature selecti ...
View/Open - Bangladesh University of Engineering and Technology
... process to discover all possible desired item sets of that group. To find likelihood of disease, we have developed constraint k-Means-Mode clustering algorithm. Due to high dimensionality of medical data, if clustering is done based on all the attributes of medical domain, resultant clusters will no ...
... process to discover all possible desired item sets of that group. To find likelihood of disease, we have developed constraint k-Means-Mode clustering algorithm. Due to high dimensionality of medical data, if clustering is done based on all the attributes of medical domain, resultant clusters will no ...
Opinion Mining
... graph-theoretically analyze three types of semantic networks: word associations, WordNet, and Roget’s thesaurus Conclusion: they have a small-world structure, characterized by sparse connectivity, short average path-lengths between words, and strong local clustering. In addition, the distributio ...
... graph-theoretically analyze three types of semantic networks: word associations, WordNet, and Roget’s thesaurus Conclusion: they have a small-world structure, characterized by sparse connectivity, short average path-lengths between words, and strong local clustering. In addition, the distributio ...
An Evaluation of the Use of Diversity to Improve the
... In recent years there has been a big focus on accuracy in recommender systems but the challenge of dealing with accurate but poor value recommendations is becoming more prominent. Introducing diversity into recommendations systems is viewed as one approach to addressing this challenge. ...
... In recent years there has been a big focus on accuracy in recommender systems but the challenge of dealing with accurate but poor value recommendations is becoming more prominent. Introducing diversity into recommendations systems is viewed as one approach to addressing this challenge. ...
Early Classification on Time Series
... Generally, most existing time series classification methods transform a time series into a set of features and then apply conventional classification methods on the feature vectors. To apply feature based methods on simple time series, usually, before feature selection, time series data needs to be ...
... Generally, most existing time series classification methods transform a time series into a set of features and then apply conventional classification methods on the feature vectors. To apply feature based methods on simple time series, usually, before feature selection, time series data needs to be ...
Spatial outlier detection based on iterative self
... outlier detection in the literature: distribution-based, clusteringbased, distance-based, density-based, and depth-based methods. Distribution-based approaches are primarily concentrated on the standard statistical distribution models. Some representative distribution models like Gaussian or Poisson ...
... outlier detection in the literature: distribution-based, clusteringbased, distance-based, density-based, and depth-based methods. Distribution-based approaches are primarily concentrated on the standard statistical distribution models. Some representative distribution models like Gaussian or Poisson ...
Knowledge Discovery over the Deep Web, Semantic Web and XML
... [Bri00] BrightPlanet. The deep Web: Surfacing hidden value. White paper, July 2000. [CHZ05] K. C.-C. Chang, B. He, and Z. Zhang. Towards large scale integration: Building a metaquerier over databases on the Web. In Proc. CIDR, Asilomar, USA, Jan. 2005. [CKGS06] C.-H. Chang, M. Kayed, M. R. Girgis, a ...
... [Bri00] BrightPlanet. The deep Web: Surfacing hidden value. White paper, July 2000. [CHZ05] K. C.-C. Chang, B. He, and Z. Zhang. Towards large scale integration: Building a metaquerier over databases on the Web. In Proc. CIDR, Asilomar, USA, Jan. 2005. [CKGS06] C.-H. Chang, M. Kayed, M. R. Girgis, a ...
![tutorial[1]. - Penn State Department of Statistics](http://s1.studyres.com/store/data/004674200_1-8c1c5165352ac8ebcae7f6e62d75bf5e-300x300.png)
tutorial[1]. - Penn State Department of Statistics
... • Constraints are specified to focus on only interesting portions of database – Example: find association rules where the prices of items are at most 200 dollars (max < 200) • Incorporating constraints can result in efficiency – Anti-monotonicity: • When an itemset violates the constraint, so does a ...
... • Constraints are specified to focus on only interesting portions of database – Example: find association rules where the prices of items are at most 200 dollars (max < 200) • Incorporating constraints can result in efficiency – Anti-monotonicity: • When an itemset violates the constraint, so does a ...
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.