Using Anonymized Data for Classification
... In many cases, anonymized data instances contain values that are generalized based on some value generalization hierarchy (e.g., see Figure 1). The main question we address is how to utilize anonymized data sets for data mining purposes. Clearly, we can represent each generalization as a discrete va ...
... In many cases, anonymized data instances contain values that are generalized based on some value generalization hierarchy (e.g., see Figure 1). The main question we address is how to utilize anonymized data sets for data mining purposes. Clearly, we can represent each generalization as a discrete va ...
02dw
... • Data cube can be viewed as a lattice of cuboids 1. The bottom-most cuboid is the base cuboid 2. The top-most cuboid (apex) contains only one cell 3. How many cuboids in an n-dimensional cube with L levels? n ...
... • Data cube can be viewed as a lattice of cuboids 1. The bottom-most cuboid is the base cuboid 2. The top-most cuboid (apex) contains only one cell 3. How many cuboids in an n-dimensional cube with L levels? n ...
Recent Themes in Case-Based Reasoning and Knowledge Discovery
... and Smyth 2001). Broadly viewed, it encompasses the automated learning of new trends and associations from data as well as novel characterizations and explanations of data. Some functionalities are well defined and researched, including: • Classification / Prediction. Classification is a supervised ...
... and Smyth 2001). Broadly viewed, it encompasses the automated learning of new trends and associations from data as well as novel characterizations and explanations of data. Some functionalities are well defined and researched, including: • Classification / Prediction. Classification is a supervised ...
Lecture 6
... increased and a new smaller CF tree is constructed. 2. Apply another global clustering approach applied to the leaf nodes in the CF tree. Here each leaf node is treated as a single point for clustering. 3. The last phase (which is optional) re-clusters all points by placing them in the cluster which ...
... increased and a new smaller CF tree is constructed. 2. Apply another global clustering approach applied to the leaf nodes in the CF tree. Here each leaf node is treated as a single point for clustering. 3. The last phase (which is optional) re-clusters all points by placing them in the cluster which ...
An Overview of Data Mining Techniques
... By transforming the predictors by squaring, cubing or taking their square root it is possible to use the same general regression methodology and now create much more complex models that are no longer simple shaped like lines. This is called non-linear regression. A model of just one predictor might ...
... By transforming the predictors by squaring, cubing or taking their square root it is possible to use the same general regression methodology and now create much more complex models that are no longer simple shaped like lines. This is called non-linear regression. A model of just one predictor might ...
Mining Hierarchies of Correlation Clusters
... are not able to capture local data correlations and find clusters of correlated objects. Pattern-based clustering methods [16, 15, 12, 11] aim at grouping objects that exhibit a similar trend in a subset of attributes into clusters rather than objects with low distance. This problem is also known as ...
... are not able to capture local data correlations and find clusters of correlated objects. Pattern-based clustering methods [16, 15, 12, 11] aim at grouping objects that exhibit a similar trend in a subset of attributes into clusters rather than objects with low distance. This problem is also known as ...
Fuzzy adaptive resonance theory: Applications and
... Today’s need for data analytic techniques is great. Biology has been the muse for data processing and optimization. Numerous methods created during the latter half of the 20th century were biologically inspired, (e.g., artificial neural networks, particle swarms, fuzzy logic, genetic and evolutionar ...
... Today’s need for data analytic techniques is great. Biology has been the muse for data processing and optimization. Numerous methods created during the latter half of the 20th century were biologically inspired, (e.g., artificial neural networks, particle swarms, fuzzy logic, genetic and evolutionar ...
A Novel Classification Approach for C2C E
... 2) Decision tree C4.5 Decision tree is a kind of decision support techniques that uses a tree-like graph or model of decisions and their possible consequences. In machine learning, decision tree is a predictive model that is a mapping from observations about an item to conclusions about its target v ...
... 2) Decision tree C4.5 Decision tree is a kind of decision support techniques that uses a tree-like graph or model of decisions and their possible consequences. In machine learning, decision tree is a predictive model that is a mapping from observations about an item to conclusions about its target v ...
View PDF
... Res.J.Recent.Sci The general access pattern tracking analyzes the web logs to understand access patterns and trends. These analyses can shed light on better structure and grouping of resource providers. Many web analysis tools existed but they are limited and usually unsatisfactory. We have designed ...
... Res.J.Recent.Sci The general access pattern tracking analyzes the web logs to understand access patterns and trends. These analyses can shed light on better structure and grouping of resource providers. Many web analysis tools existed but they are limited and usually unsatisfactory. We have designed ...
CURIO : A Fast Outlier and Outlier Cluster Detection Algorithm for
... examines point neighborhoods from a topologically connected, rather than distance based perspective. To alleviate the curse of dimensionality, research has also been undertaken into the use of lowdimensional projections to identify outliers. Aggarwal & Yu (2001) adopt an evolutionary algorithm to di ...
... examines point neighborhoods from a topologically connected, rather than distance based perspective. To alleviate the curse of dimensionality, research has also been undertaken into the use of lowdimensional projections to identify outliers. Aggarwal & Yu (2001) adopt an evolutionary algorithm to di ...
An Overview of Data Mining Techniques
... By transforming the predictors by squaring, cubing or taking their square root it is possible to use the same general regression methodology and now create much more complex models that are no longer simple shaped like lines. This is called non-linear regression. A model of just one predictor might ...
... By transforming the predictors by squaring, cubing or taking their square root it is possible to use the same general regression methodology and now create much more complex models that are no longer simple shaped like lines. This is called non-linear regression. A model of just one predictor might ...
MKTG 630 Predictive Analytics and Data Mining
... problems. At no time has there been a greater need for quantitatively skilled and analytically minded managerial expertise. This need is being evidenced in a transformation of MBA programs across the country and around the globe. Universities are beginning to offer graduate courses or entire MBA pro ...
... problems. At no time has there been a greater need for quantitatively skilled and analytically minded managerial expertise. This need is being evidenced in a transformation of MBA programs across the country and around the globe. Universities are beginning to offer graduate courses or entire MBA pro ...
Mining Interval Time Series
... sliding window to limit the comparisons to only the patterns within the window at any one time. This approach significantly reduces the complexity. However, choosing an appropriate size for the window can be a difficult task. As we will discuss later, our technique does not have this problem. The re ...
... sliding window to limit the comparisons to only the patterns within the window at any one time. This approach significantly reduces the complexity. However, choosing an appropriate size for the window can be a difficult task. As we will discuss later, our technique does not have this problem. The re ...
Slides - Microsoft
... top node is associated with the entire target space. – Each non-leaf node divides its region into four equal sized quadrants – Leaf nodes have between zero and some fixed maximum number of points (set to 1 in example). ...
... top node is associated with the entire target space. – Each non-leaf node divides its region into four equal sized quadrants – Leaf nodes have between zero and some fixed maximum number of points (set to 1 in example). ...
Unveiling the complexity of human mobility by querying and mining
... that analysts reason about high-level concepts, such as systematic vs. occasional movement behavior, purpose of a trip, and home-work commuting patterns. Accordingly, the mainstream analytical tools of transportation engineering, such as origin/destination matrices, are based on semantically rich da ...
... that analysts reason about high-level concepts, such as systematic vs. occasional movement behavior, purpose of a trip, and home-work commuting patterns. Accordingly, the mainstream analytical tools of transportation engineering, such as origin/destination matrices, are based on semantically rich da ...
CHAPTER-15 Mining Multilevel Association Rules
... where X is a variable representing customers who purchased items in ABCompany transcations .Following the terminology used in multidimensional database ,we refer to each distinct predicate in a rule as a dimensional.Hence,we can refer to the above rule as a single-dimensional or intradimension assoc ...
... where X is a variable representing customers who purchased items in ABCompany transcations .Following the terminology used in multidimensional database ,we refer to each distinct predicate in a rule as a dimensional.Hence,we can refer to the above rule as a single-dimensional or intradimension assoc ...
Multivariate Maximal Correlation Analysis
... that maximize their correlation (measured by C ORR). Following Definition 1, to search for maximal correlation, we need to solve an optimization problem over a search space whose size is potentially exponential to the number of dimensions. The search space in general does not exhibit structure that ...
... that maximize their correlation (measured by C ORR). Following Definition 1, to search for maximal correlation, we need to solve an optimization problem over a search space whose size is potentially exponential to the number of dimensions. The search space in general does not exhibit structure that ...
sequential pattern mining with approximated constraints
... As pointed by some authors [Hipp 2002], when used incautiously, constrained pattern mining may reduce to a hypothesis-testing task. Note that, if blindly applied, it avoids the discovery of unknown and unexpected patterns, which is the first and foremost data mining’s goal. Indeed, the formal langua ...
... As pointed by some authors [Hipp 2002], when used incautiously, constrained pattern mining may reduce to a hypothesis-testing task. Note that, if blindly applied, it avoids the discovery of unknown and unexpected patterns, which is the first and foremost data mining’s goal. Indeed, the formal langua ...
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.