
K-Means - Columbia Statistics
... • Dissimilarity/Similarity metric: Similarity is expressed in terms of a distance function, which is typically metric: d(i, j) • There is a separate “quality” function that measures the “goodness” of a cluster. • The definitions of distance functions are usually very different for interval-scaled, b ...
... • Dissimilarity/Similarity metric: Similarity is expressed in terms of a distance function, which is typically metric: d(i, j) • There is a separate “quality” function that measures the “goodness” of a cluster. • The definitions of distance functions are usually very different for interval-scaled, b ...
Data Cleaning Using Clustering Based Data Mining Technique
... The main idea behind this algorithm is based on an observation that in most data sets there is a certain number of values having large number of occurrences within the data sets and a very large number of attributes with a very low number of occurrences. Therefore, the most representative values may ...
... The main idea behind this algorithm is based on an observation that in most data sets there is a certain number of values having large number of occurrences within the data sets and a very large number of attributes with a very low number of occurrences. Therefore, the most representative values may ...
Moving Objects Databases
... experiments conducted. One was update queries randomly generated for set of 10,000 cars for timestamps 1 to 4 at rates 1% and 5%. Other experiment was on different data sizes, 5k, 10k, 20k and 30k cars. Updates were taken at 1% and 5% rates and the algorithm proved to give most stable results for al ...
... experiments conducted. One was update queries randomly generated for set of 10,000 cars for timestamps 1 to 4 at rates 1% and 5%. Other experiment was on different data sizes, 5k, 10k, 20k and 30k cars. Updates were taken at 1% and 5% rates and the algorithm proved to give most stable results for al ...
A Framework for a Video Analysis Tool for Suspicious Event Detection
... Rapidly sift through the data and discard unwanted data for later use and analysis (non-real-time data mining) Data mining techniques need to meet timing constraints Quality of service (QoS) tradeoffs among timeliness, precision and accuracy Presentation of results, visualization, real-time alerts a ...
... Rapidly sift through the data and discard unwanted data for later use and analysis (non-real-time data mining) Data mining techniques need to meet timing constraints Quality of service (QoS) tradeoffs among timeliness, precision and accuracy Presentation of results, visualization, real-time alerts a ...
Introduction to Data Mining
... • Data Mining is a powerful technology still undiscovered by many IT and database professionals • Turns data into intelligence • SQL Server 2005 and 2008 Analysis Services have been created with you in mind • Let’s mine for valuable gems of knowledge in our databases! ...
... • Data Mining is a powerful technology still undiscovered by many IT and database professionals • Turns data into intelligence • SQL Server 2005 and 2008 Analysis Services have been created with you in mind • Let’s mine for valuable gems of knowledge in our databases! ...
Top 10 Data Mining Mistakes by John Elder
... • Developing a transformational data mining service can be a major undertaking requiring extensive energy and resources • Unless there is complete organizational investment, the task can be overwhelming and quickly result in frustration and failure • Starting small allows the organization to ge ...
... • Developing a transformational data mining service can be a major undertaking requiring extensive energy and resources • Unless there is complete organizational investment, the task can be overwhelming and quickly result in frustration and failure • Starting small allows the organization to ge ...
Data Foundations
... Data Attributes Describing data content and characteristics Representing data dimensions Set of all attributes: attribute vector or ...
... Data Attributes Describing data content and characteristics Representing data dimensions Set of all attributes: attribute vector or ...
Visual decisions in the analysis of customers online shopping
... over the last two years. While the previous years saw a contraction in marketing budgets, the digital marketplace in Europe continues growing. With its growth there comes innovation – from advanced planning tools to sophisticated targeting techniques. Social media grew immensely in 2010 and saturate ...
... over the last two years. While the previous years saw a contraction in marketing budgets, the digital marketplace in Europe continues growing. With its growth there comes innovation – from advanced planning tools to sophisticated targeting techniques. Social media grew immensely in 2010 and saturate ...
Using AK-Mode Algorithm to Cluster OLAP Requirements
... process that provides the exploration, explication and prediction capabilities. Another data mining system DBMiner was presented in [10]. This latter integrates different data mining functions such as characterization, comparison, association, classification, prediction and clustering, as well as it ...
... process that provides the exploration, explication and prediction capabilities. Another data mining system DBMiner was presented in [10]. This latter integrates different data mining functions such as characterization, comparison, association, classification, prediction and clustering, as well as it ...
Algorithms for Information Retrieval. Introduction
... annotation is time-consuming, laborious and expensive; to address this, there has been a large amount of research done on automatic image annotation. Additionally, the increase in social web ...
... annotation is time-consuming, laborious and expensive; to address this, there has been a large amount of research done on automatic image annotation. Additionally, the increase in social web ...
Newer Database Topics - University of Manitoba
... Goal: To discover unknown relationships in the data that can be used to make better decisions. Exploratory analysis. A bottom-up approach that scans the data to find relationships Some statistical routines, but they are not sufficient Statistics relies on averages Sometimes the important ...
... Goal: To discover unknown relationships in the data that can be used to make better decisions. Exploratory analysis. A bottom-up approach that scans the data to find relationships Some statistical routines, but they are not sufficient Statistics relies on averages Sometimes the important ...
20030409-Grid-Redman
... • Heterogeneity leads to data usability problems • One approach: Standard data formats Difficult to implement and enforce Can’t anticipate all needs Some data can’t be modeled or is lost in translation The cost of converting legacy data • A better approach: Interchange Technologies Earth S ...
... • Heterogeneity leads to data usability problems • One approach: Standard data formats Difficult to implement and enforce Can’t anticipate all needs Some data can’t be modeled or is lost in translation The cost of converting legacy data • A better approach: Interchange Technologies Earth S ...
AE044209211
... different sub-datasets and next, accessible well predictable clustering method developed for various types of datasets is applied to produce equivalent clusters. Finally, the clustering results on the categorical and numeric dataset are combined as a categorical dataset, on which that clustering met ...
... different sub-datasets and next, accessible well predictable clustering method developed for various types of datasets is applied to produce equivalent clusters. Finally, the clustering results on the categorical and numeric dataset are combined as a categorical dataset, on which that clustering met ...
Biased Quantile
... It finds a local minimum of the objective function that is average sum of squared distance of points from the cluster center. Begin by picking k points randomly from the data Repeatedly alternate two phases: Assign each input point to its closest center Compute centroid of each cluster (average poin ...
... It finds a local minimum of the objective function that is average sum of squared distance of points from the cluster center. Begin by picking k points randomly from the data Repeatedly alternate two phases: Assign each input point to its closest center Compute centroid of each cluster (average poin ...
Data Mining: introduction - UIC
... week after the item in question has been returned. Cheating: Cheating will not be tolerated. All work you submitted must be entirely your own. Any suspicious similarities between students' work will be recorded and brought to the attention of the Dean. The MINIMUM penalty for any student found cheat ...
... week after the item in question has been returned. Cheating: Cheating will not be tolerated. All work you submitted must be entirely your own. Any suspicious similarities between students' work will be recorded and brought to the attention of the Dean. The MINIMUM penalty for any student found cheat ...
Online Unsupervised State Recognition in Sensor Data
... We define a state as a consecutively repeating pattern of symbols that has several (significant) occurrences along the time series. In previous works, the term pattern can be found termed as primitive shape [8], frequent temporal pattern [9] and motif [10], and what we call states is more similar to ...
... We define a state as a consecutively repeating pattern of symbols that has several (significant) occurrences along the time series. In previous works, the term pattern can be found termed as primitive shape [8], frequent temporal pattern [9] and motif [10], and what we call states is more 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.