... are usually difficult to obtain. Memory-based CF such as item-CF and user-CF can’t guarantee the performance due to the data sparsity and lack of explicit optimization objective. Although model-based CF methods such as matrix factorization are known to provide accurate recommendation performances, m ...
A Conceptual Framework for Data Mining and Knowledge
... People have been collecting and organizing data from stone ages. In the earlier days data were collected and recorded in one way or the other mainly for record keeping purposes. With the advancement in computational technology in general and storage technology in particular data collection and their ...
... People have been collecting and organizing data from stone ages. In the earlier days data were collected and recorded in one way or the other mainly for record keeping purposes. With the advancement in computational technology in general and storage technology in particular data collection and their ...
Cluster Ensembles for Big Data Mining Problems
... from autonomous and decentralized sources, thus its dimensionality is heterogeneous and diverse, and generally involves privacy issues. On the other hand, algorithms for mining data such as clustering methods, have particular characteristics that make them useful for different types of data mining p ...
... from autonomous and decentralized sources, thus its dimensionality is heterogeneous and diverse, and generally involves privacy issues. On the other hand, algorithms for mining data such as clustering methods, have particular characteristics that make them useful for different types of data mining p ...
comparative investigations and performance analysis of
... Clustering can be considered the most important unsupervised learning problem. So, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data. A loose definition of clustering could be the process of organizing objects into groups whose members are simil ...
... Clustering can be considered the most important unsupervised learning problem. So, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data. A loose definition of clustering could be the process of organizing objects into groups whose members are simil ...
E-Governance in Elections: Implementation of Efficient Decision
... y is a vector of n predictions and y is the vector of observed values corresponding to the inputs to the function which generated the predictions. The main objective of the proposed algorithm is to reduce classification error and minimize retrieval process in comparison with available dataset. This ...
... y is a vector of n predictions and y is the vector of observed values corresponding to the inputs to the function which generated the predictions. The main objective of the proposed algorithm is to reduce classification error and minimize retrieval process in comparison with available dataset. This ...
Introduction to Data Mining
... • For classification. this can mean that there are not enough data objects to allow the creation of a model that reliably assigns a class to all possible objects. ...
... • For classification. this can mean that there are not enough data objects to allow the creation of a model that reliably assigns a class to all possible objects. ...
Supporting Exploratory Search by User
... different tools. A problem appears with the entity identification: There must be a consensus between all tools about how to identify equal entities. This can be achieved by finding a global convention for naming entities which takes different contexts and knowledge domains as well as different infor ...
... different tools. A problem appears with the entity identification: There must be a consensus between all tools about how to identify equal entities. This can be achieved by finding a global convention for naming entities which takes different contexts and knowledge domains as well as different infor ...
abstract - Chennaisunday.com
... one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instea ...
... one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instea ...
Project Presenation
... HIERARCHICAL AGGLOMERATIVE CLUSTERING Initially, each item is considered a cluster. The closest pair is chosen. Those two clusters are merged. Each iteration reduces one cluster. Continues till terminating condition satisfies. ...
... HIERARCHICAL AGGLOMERATIVE CLUSTERING Initially, each item is considered a cluster. The closest pair is chosen. Those two clusters are merged. Each iteration reduces one cluster. Continues till terminating condition satisfies. ...
PDF - International Journal of Advanced Research
... In India, sixty percentages of people are the followers of astrology from the birth to death. They believe that astrology can solve the confusions in their life. The publications related to astrology in popular magazines shows the accessibility of this traditional science in common people. If we con ...
... In India, sixty percentages of people are the followers of astrology from the birth to death. They believe that astrology can solve the confusions in their life. The publications related to astrology in popular magazines shows the accessibility of this traditional science in common people. If we con ...
References and further reading (Tutorial on Statistically Sound
... iterative data mining. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 379-388. ACM, 2009. • R. Hubbard and M.J. Bayarri: P Values are not Error Probabilities. Discussion papers series 2003-26, Department of Statistical Science, University ...
... iterative data mining. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 379-388. ACM, 2009. • R. Hubbard and M.J. Bayarri: P Values are not Error Probabilities. Discussion papers series 2003-26, Department of Statistical Science, University ...
Protecting Data in a Collaborative Environment
... Shared data in collaborative environments Intellectual property Personal and private National security ...
... Shared data in collaborative environments Intellectual property Personal and private National security ...
parameter-free cluster detection in spatial databases and its
... In GIS and digital cartography, respectively, there is a growing demand for such techniques: huge spatial data sets are being acquired and have to be kept up to date at ever increasing cycles; furthermore, information of different levels of detail is required in order to compensate for the requireme ...
... In GIS and digital cartography, respectively, there is a growing demand for such techniques: huge spatial data sets are being acquired and have to be kept up to date at ever increasing cycles; furthermore, information of different levels of detail is required in order to compensate for the requireme ...
Sharing RapidMiner Workflows and Experiments with OpenML
... terms of predictive accuracy. However, when measuring the Area Under the ROC Curve, most RapidMiner algorithms perform somewhat better, see for example the Support Vector Machines as shown in Figure 3(b). The fact that two implementations of the same algorithm yield very different results can have v ...
... terms of predictive accuracy. However, when measuring the Area Under the ROC Curve, most RapidMiner algorithms perform somewhat better, see for example the Support Vector Machines as shown in Figure 3(b). The fact that two implementations of the same algorithm yield very different results can have v ...
Lesson 6: Data Mining
... the individual level. Instead of looking at which candidate will win the Presidential election in the state of Ohio, which is forecasting. It looks at the individual level. Which person is voting for or against. Predicts which individuals can be persuaded, which ones will not change, etc. Now with t ...
... the individual level. Instead of looking at which candidate will win the Presidential election in the state of Ohio, which is forecasting. It looks at the individual level. Which person is voting for or against. Predicts which individuals can be persuaded, which ones will not change, etc. Now with t ...
Test
... between the coordinates of a pair of objects. This is most generally known as the Pythagorean theorem. • The taxicab metric is also known as rectilinear distance, L1 distance or L1 norm, city block distance, Manhattan distance, or Manhattan length, with the corresponding variations in the name of th ...
... between the coordinates of a pair of objects. This is most generally known as the Pythagorean theorem. • The taxicab metric is also known as rectilinear distance, L1 distance or L1 norm, city block distance, Manhattan distance, or Manhattan length, with the corresponding variations in the name of th ...
Microsoft PowerPoint Presentation: 07_1_Lecture
... • Given the set S of n points, we can find pmax and pmin in O(n) time. • We can find all the points above and below pmax pmin also in O(n) time. • We can compute the convex hull of all the points above pmax pmin and call this as UH(S). • Similarly, we can compute the convex hull of all the points be ...
... • Given the set S of n points, we can find pmax and pmin in O(n) time. • We can find all the points above and below pmax pmin also in O(n) time. • We can compute the convex hull of all the points above pmax pmin and call this as UH(S). • Similarly, we can compute the convex hull of all the points be ...
LSGI4241A
... Please read the notes at the end of the table carefully before completing the form. Subject Code ...
... Please read the notes at the end of the table carefully before completing the form. Subject Code ...
Nonlinear dimensionality reduction
High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space. If the manifold is of low enough dimension, the data can be visualised in the low-dimensional space.Below is a summary of some of the important algorithms from the history of manifold learning and nonlinear dimensionality reduction (NLDR). Many of these non-linear dimensionality reduction methods are related to the linear methods listed below. Non-linear methods can be broadly classified into two groups: those that provide a mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa), and those that just give a visualisation. In the context of machine learning, mapping methods may be viewed as a preliminary feature extraction step, after which pattern recognition algorithms are applied. Typically those that just give a visualisation are based on proximity data – that is, distance measurements.