CrowdMiner: Mining association rules from the crowd
... of patterns in an unknown domain. While there has been vast research on data mining, and many recent developments of crowdsourcing platforms (e.g., [8, 6]), there has been no previous work integrating the two. As a motivating example, consider the study of people’s well-being practices, such as spor ...
... of patterns in an unknown domain. While there has been vast research on data mining, and many recent developments of crowdsourcing platforms (e.g., [8, 6]), there has been no previous work integrating the two. As a motivating example, consider the study of people’s well-being practices, such as spor ...
MobiVis: A Visualization System for Exploring Mobile Data
... discovery of mobile data. We address the challenges of visualizing complex social-spatial-temporal data in its design and implementation. In this section, we first introduce a methodology to formulate the data into a heterogeneous network. Next, we discuss the interactive time chart and ontology gra ...
... discovery of mobile data. We address the challenges of visualizing complex social-spatial-temporal data in its design and implementation. In this section, we first introduce a methodology to formulate the data into a heterogeneous network. Next, we discuss the interactive time chart and ontology gra ...
CLARANS: a method for clustering objects for spatial data mining
... one nonspatial dominant algorithm to extract high-level relationships between spatial and nonspatial data. However, both algorithms suffer from the following problems. First, the user or an expert must provide the algorithms with spatial concept hierarchies, which may not be available in many applic ...
... one nonspatial dominant algorithm to extract high-level relationships between spatial and nonspatial data. However, both algorithms suffer from the following problems. First, the user or an expert must provide the algorithms with spatial concept hierarchies, which may not be available in many applic ...
pdf 167K
... In this case it is possible to join the set in a single pass. To speed up the CPU operations, for each stripe a main memory data structure, the ε-kdB-tree is constructed which also partitions the data set according to the other dimensions until a defined node capacity is reached. For each dimension, ...
... In this case it is possible to join the set in a single pass. To speed up the CPU operations, for each stripe a main memory data structure, the ε-kdB-tree is constructed which also partitions the data set according to the other dimensions until a defined node capacity is reached. For each dimension, ...
Data Warehousing and Data Mining
... For the data modeling part of the course the starting point is to understand the basics of ER modeling and also its limitation for creating an enterprise wide data model for decision-making purposes. The Entity Relationship (ER) diagram is commonly being used to create transaction-oriented relationa ...
... For the data modeling part of the course the starting point is to understand the basics of ER modeling and also its limitation for creating an enterprise wide data model for decision-making purposes. The Entity Relationship (ER) diagram is commonly being used to create transaction-oriented relationa ...
pdf (preprint)
... databases for different spatial granularities and multiple temporal states has increased considerably (for census data see e.g. Martin 2006). Such databases typically contain hidden and unexpected information, which cannot be discovered using traditional statistical methods that require a priori hyp ...
... databases for different spatial granularities and multiple temporal states has increased considerably (for census data see e.g. Martin 2006). Such databases typically contain hidden and unexpected information, which cannot be discovered using traditional statistical methods that require a priori hyp ...
Data Mining Tasks
... Association Rule Discovery: Definition • Given a set of records each of which contain some number of items from a given collection; – Produce dependency rules which will predict occurrence of an item based on occurrences of other items. ...
... Association Rule Discovery: Definition • Given a set of records each of which contain some number of items from a given collection; – Produce dependency rules which will predict occurrence of an item based on occurrences of other items. ...
DM - ITTC
... What is in Your Data What kinds of data quality problems? How can we detect problems with the data? What can we do about these problems? Examples of data quality problems: ...
... What is in Your Data What kinds of data quality problems? How can we detect problems with the data? What can we do about these problems? Examples of data quality problems: ...
data mining
... Create a root node and assign all of the training data to it. Select the best splitting attribute. Add a branch to the root node for each value of the split. Split the data into mutually exclusive subsets along the lines of the specific split. Repeat the steps 2 and 3 for each and every leaf node un ...
... Create a root node and assign all of the training data to it. Select the best splitting attribute. Add a branch to the root node for each value of the split. Split the data into mutually exclusive subsets along the lines of the specific split. Repeat the steps 2 and 3 for each and every leaf node un ...
Data Mining: Past, Present and Future
... mechanisms whereby computers can learn (for example, one focus of early work on machine learning was computer programmes that could learn to play chess). Machine learning can thus be viewed as a technology, whereas data mining, and by extension KDD, as an application. Traditionally data mining techn ...
... mechanisms whereby computers can learn (for example, one focus of early work on machine learning was computer programmes that could learn to play chess). Machine learning can thus be viewed as a technology, whereas data mining, and by extension KDD, as an application. Traditionally data mining techn ...
Mining Patterns from large Star Schemas based on Streaming
... describing an event or occurrence, characterized by a particular combination of dimensions. In turn, each dimension aggregates a set of attributes for a same domain property or constraint [9]. In recent years, the most common mining techniques for a single table have been extended to the multi-relat ...
... describing an event or occurrence, characterized by a particular combination of dimensions. In turn, each dimension aggregates a set of attributes for a same domain property or constraint [9]. In recent years, the most common mining techniques for a single table have been extended to the multi-relat ...
Data Profiling with Metanome
... a graphical visualization of an inclusion dependency graph, for instance, Metanome must know that the output contains inclusion dependencies and it must distinguish their dependent and referenced attributes. The most important types of metadata supported currently are unique column combinations (UCC ...
... a graphical visualization of an inclusion dependency graph, for instance, Metanome must know that the output contains inclusion dependencies and it must distinguish their dependent and referenced attributes. The most important types of metadata supported currently are unique column combinations (UCC ...
Detecting Adversarial Advertisements in the Wild
... recall. We then train a set of more finely-grained models to detect each of these more difficult classes with high precision, using the one-vs-good framework (see Figure 4). Cascade models are particularly susceptible to problems of over-fitting. We have found tightly regularizing the coarselevel mo ...
... recall. We then train a set of more finely-grained models to detect each of these more difficult classes with high precision, using the one-vs-good framework (see Figure 4). Cascade models are particularly susceptible to problems of over-fitting. We have found tightly regularizing the coarselevel mo ...
Community discovery using nonnegative matrix factorization
... hierarchical agglomerative clustering (HAC) method (Newman 2004a), the modularity optimization method (Newman 2004b) and the probabilistic method based on latent models (Zhang et al. 2007). More recently, the graph based partitioning methods (von Luxburg 2007; Weiss 1999) have aroused considerable i ...
... hierarchical agglomerative clustering (HAC) method (Newman 2004a), the modularity optimization method (Newman 2004b) and the probabilistic method based on latent models (Zhang et al. 2007). More recently, the graph based partitioning methods (von Luxburg 2007; Weiss 1999) have aroused considerable i ...
analyse input data
... • This data view is part of the rank-by-feature framework • Data belonging to one column (variable) is displayed as a histogram + box plot – Histogram shows the scale and skewness – Box plot shows the data distribution, center and spread ...
... • This data view is part of the rank-by-feature framework • Data belonging to one column (variable) is displayed as a histogram + box plot – Histogram shows the scale and skewness – Box plot shows the data distribution, center and spread ...
DU22738742
... One of the most widely used areas of data mining for the banking industry is marketing. The bank‟s marketing department can use data mining to analyse customer databases. Data mining carry various analyses on collected data to determine the consumer behavior with reference to product, price and dist ...
... One of the most widely used areas of data mining for the banking industry is marketing. The bank‟s marketing department can use data mining to analyse customer databases. Data mining carry various analyses on collected data to determine the consumer behavior with reference to product, price and dist ...
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.