Frequency-aware Similarity Measures - Hasso-Plattner
... problem. Suitable similarity measures help to find duplicates and thus cleanse a data set, or they can help finding nearest neighbors to answer search queries. The problem comprises two main difficulties: First, the representations of same real-world objects might differ due to typos, outdated value ...
... problem. Suitable similarity measures help to find duplicates and thus cleanse a data set, or they can help finding nearest neighbors to answer search queries. The problem comprises two main difficulties: First, the representations of same real-world objects might differ due to typos, outdated value ...
Data Mining: Concepts and Techniques
... data lineage (history of migrated data and transformation path), currency of data (active, archived, or purged), monitoring information (warehouse usage statistics, error reports, audit trails) ...
... data lineage (history of migrated data and transformation path), currency of data (active, archived, or purged), monitoring information (warehouse usage statistics, error reports, audit trails) ...
Online Data Mining
... Moreover, traditional data cubes support only dimensions of categorical data and measures of numerical data. In practice, the dimensions of a data cube can be of numerical, spatial, and multimedia data. The measures of a cube can also be of spatial and multimedia aggregations, or collections of them ...
... Moreover, traditional data cubes support only dimensions of categorical data and measures of numerical data. In practice, the dimensions of a data cube can be of numerical, spatial, and multimedia data. The measures of a cube can also be of spatial and multimedia aggregations, or collections of them ...
Explore RFM approaches using SAS
... With the development of modern data mining approaches, researchers consider the incorporation of RFM variables into modeling techniques, like clustering (Hosseini et al., 2010), neural network and decision tree (Olson et al., 2009), support vector machine (Zhang, 2012) or sequence of multiple data m ...
... With the development of modern data mining approaches, researchers consider the incorporation of RFM variables into modeling techniques, like clustering (Hosseini et al., 2010), neural network and decision tree (Olson et al., 2009), support vector machine (Zhang, 2012) or sequence of multiple data m ...
Data Mining Concepts, Techniques and Applications
... Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns Clustering based on the principle: maximizing the intra-class similarity and minimizing the interclass similarity Data Mining: Concepts, Techniques and Applications ...
... Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns Clustering based on the principle: maximizing the intra-class similarity and minimizing the interclass similarity Data Mining: Concepts, Techniques and Applications ...
A Comparison of Several Approaches to Missing Attribute Values in
... The house data set, which has 203 examples that contain unknown attribute values, consists of votes of 435 congressmen in 1984 on 16 key-issues (yes or no). The im85 data set is from a 1985 Automobile Imports Database, and it consists of three types of entities: a) the specification of an auto in te ...
... The house data set, which has 203 examples that contain unknown attribute values, consists of votes of 435 congressmen in 1984 on 16 key-issues (yes or no). The im85 data set is from a 1985 Automobile Imports Database, and it consists of three types of entities: a) the specification of an auto in te ...
Contextual Itemset Mining in DBpedia
... The above definitions provide us with a theoretical framework for CFP mining. In the rest of this section, we design the algorithm that extracts CFPs from DBpedia. This algorithm is inspired from the one that was proposed in [16] for mining contextual frequent sequential patterns (i.e., a variation ...
... The above definitions provide us with a theoretical framework for CFP mining. In the rest of this section, we design the algorithm that extracts CFPs from DBpedia. This algorithm is inspired from the one that was proposed in [16] for mining contextual frequent sequential patterns (i.e., a variation ...
Mining Spatial Association Rules in Census Data
... are exploratory and when applied to spatially correlated data some of them are of unknown reliability having been developed initially, like so many areas in statistics, for situations where observations are independent [9]. This contrasts with the nature of spatial data where spatial objects are inf ...
... are exploratory and when applied to spatially correlated data some of them are of unknown reliability having been developed initially, like so many areas in statistics, for situations where observations are independent [9]. This contrasts with the nature of spatial data where spatial objects are inf ...
Over viewing issues of data mining with highlights of data
... format. They must resolve such problems as naming conflicts and inconsistencies among units of measure. When they achieve this, they are said to be integrated. Nonvolatile: Nonvolatile means that, once entered into the data warehouse, data should not change. This is logical because the purpose of a ...
... format. They must resolve such problems as naming conflicts and inconsistencies among units of measure. When they achieve this, they are said to be integrated. Nonvolatile: Nonvolatile means that, once entered into the data warehouse, data should not change. This is logical because the purpose of a ...
KMC-20050525 - Kansas State University
... • Compute conditional probability of hypothesis h given observed data D • i.e., compute expectation over unknown h for unseen cases ...
... • Compute conditional probability of hypothesis h given observed data D • i.e., compute expectation over unknown h for unseen cases ...
as a PDF - Institut für Theoretische Informatik
... The data of a real example from the molecular biology in the last section could be classified by using a decision tree. Though we presented the separating decision tree, we left it open how this tree has been found. Therefore, we continue our course with decision tree learning. Generally speaking, d ...
... The data of a real example from the molecular biology in the last section could be classified by using a decision tree. Though we presented the separating decision tree, we left it open how this tree has been found. Therefore, we continue our course with decision tree learning. Generally speaking, d ...
Commercially Available Data Mining Tools used in the Economic
... tasks that is worth mentioning is that between outlier analysis and other tasks like clustering or classification. The latter two are not intended to find exceptions in the data available (outliers) but can also be used for these tasks because an exception can be classified in a different class (or ...
... tasks that is worth mentioning is that between outlier analysis and other tasks like clustering or classification. The latter two are not intended to find exceptions in the data available (outliers) but can also be used for these tasks because an exception can be classified in a different class (or ...
What is Data Mining?
... Enables DBAs and LOB users to readily integrate R models into production Enables R models to be integrated into BI dashboards Enables R programmers/statisticians to work against database data without knowing SQL Reduces the number of LOB help requests for SQL queries to obtain data Removes the need ...
... Enables DBAs and LOB users to readily integrate R models into production Enables R models to be integrated into BI dashboards Enables R programmers/statisticians to work against database data without knowing SQL Reduces the number of LOB help requests for SQL queries to obtain data Removes the need ...
Techniques for Web Usage Mining
... Association rules are used to find out the pages on web which are accessed together. This helps in foretelling which pages may be accessed by the user in future. The pages which are accessed together are put in a single server session. There is a specific support value based on which the web pages ...
... Association rules are used to find out the pages on web which are accessed together. This helps in foretelling which pages may be accessed by the user in future. The pages which are accessed together are put in a single server session. There is a specific support value based on which the web pages ...
Data Mining Technologies - College of Business « UNT
... then relevant sets of three or four. • These are then pruned by removing those that occur infrequently. • In an environment like a grocery store, where customers commonly buy over 100 items, rules could involve as many as 10 items. ...
... then relevant sets of three or four. • These are then pruned by removing those that occur infrequently. • In an environment like a grocery store, where customers commonly buy over 100 items, rules could involve as many as 10 items. ...
Big Data Surveillance: Introduction
... that simply function” (Zizek and Daly 2004: 97). We might say something similar of algorithmic correlations and predictions: they do not provide us with underlying, common sense explanations, but offer findings based on a level of complexity that makes them, in some cases, utterly inexplicable. The ...
... that simply function” (Zizek and Daly 2004: 97). We might say something similar of algorithmic correlations and predictions: they do not provide us with underlying, common sense explanations, but offer findings based on a level of complexity that makes them, in some cases, utterly inexplicable. The ...
LNCS 3292 - Improving Distributed Data Mining Techniques
... JAM (Java Agent for Meta-learning) [28] is an architecture developed at University of Columbia. JAM has been developed to gather information from sparse data sources and induce a global classification model. JAM technology is based on the meta-learning technique. Meta-learning makes it possible to b ...
... JAM (Java Agent for Meta-learning) [28] is an architecture developed at University of Columbia. JAM has been developed to gather information from sparse data sources and induce a global classification model. JAM technology is based on the meta-learning technique. Meta-learning makes it possible to b ...
Predicting Workers' Compensation Insurance Fraud Using SAS Enterprise Miner 5.1 and SAS Text Miner
... Preparation (SAS Institute Inc., 2004a) provide macros for finding the distance between two ZIP-code regions. The primary calculation involves using the Haversine function, which gives the distance between two locations that are specified as latitude and longitude coordinates. The unsupervised learn ...
... Preparation (SAS Institute Inc., 2004a) provide macros for finding the distance between two ZIP-code regions. The primary calculation involves using the Haversine function, which gives the distance between two locations that are specified as latitude and longitude coordinates. The unsupervised learn ...
Interactive visualization of financial data
... mapping is heat maps, which are commonly used to show temperature information of 3D objects, such that the color is set depending on the temperature at each point. In this way, four dimensions can be shown instead of just three. ...
... mapping is heat maps, which are commonly used to show temperature information of 3D objects, such that the color is set depending on the temperature at each point. In this way, four dimensions can be shown instead of just three. ...
Class Association Rule Mining with Multiple Imbalanced Attributes
... Data imbalance is often encountered in data mining especially classification and association rule mining. As integration of classification and association rule, class association rule [3] always suffers from data imbalance problem. In class association rule, the right-hand side is a predefined target cl ...
... Data imbalance is often encountered in data mining especially classification and association rule mining. As integration of classification and association rule, class association rule [3] always suffers from data imbalance problem. In class association rule, the right-hand side is a predefined target cl ...
Data Warehouse and OLAP Technology
... Star schema: A fact table in the middle connected to a set of dimension tables Snowflake schema: A refinement of star schema where some dimensional hierarchy is normalized into additonal smaller dimension tables, forming a shape similar to ...
... Star schema: A fact table in the middle connected to a set of dimension tables Snowflake schema: A refinement of star schema where some dimensional hierarchy is normalized into additonal smaller dimension tables, forming a shape similar to ...
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.