A Case of Data Mining - Global Vision Publishing House
... The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes’ theorem (1700s) and regression analysis (1800s). The increasing power of computer technology has increased data collection, storage and manipulations. As data sets hav ...
... The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes’ theorem (1700s) and regression analysis (1800s). The increasing power of computer technology has increased data collection, storage and manipulations. As data sets hav ...
Embedding Heterogeneous Data by Preserving Multiple Kernels
... Dirichlet allocation. (ii) They find a common subspace for these two extracted representations using CCA. [18] gives a multiview metric learning algorithm, which projects data points from different views into a shared subspace by trying to capture cross- and within-view similarities in this space. [2 ...
... Dirichlet allocation. (ii) They find a common subspace for these two extracted representations using CCA. [18] gives a multiview metric learning algorithm, which projects data points from different views into a shared subspace by trying to capture cross- and within-view similarities in this space. [2 ...
Data Mining: An Overview
... dependencies or relationships among data items • Classification - grouping records into meaningful subclasses or clusters • Deviation detection - discovery of significant differences between an observation and some reference – potentially correct the data – Anomalous instances, Outliers – Classes wi ...
... dependencies or relationships among data items • Classification - grouping records into meaningful subclasses or clusters • Deviation detection - discovery of significant differences between an observation and some reference – potentially correct the data – Anomalous instances, Outliers – Classes wi ...
Data Mining - Emory Math/CS Department
... Brin, S. and Page, L. The anatomy of a large-scale hypertextual Web search engine (PageRank). In Computer Networks and ISDN Systems, 1998 J. Kleinberg. Authoritative sources in a hyperlinked environment (HITS). In ACM-SIAM Symp. Discrete Algorithms, 1998 S. Chakrabarti, B. Dom, D. Gibson, J. Kleinbe ...
... Brin, S. and Page, L. The anatomy of a large-scale hypertextual Web search engine (PageRank). In Computer Networks and ISDN Systems, 1998 J. Kleinberg. Authoritative sources in a hyperlinked environment (HITS). In ACM-SIAM Symp. Discrete Algorithms, 1998 S. Chakrabarti, B. Dom, D. Gibson, J. Kleinbe ...
Data Mining in E-Commerce: A CRM Platform
... such as site statistics, user demographics and audience measurement data [9]. These extensions open new possibilities and quality in data mining applications that include bridging business and engineering fields on one hand and business and social sciences on the other hand. A statistics on the anal ...
... such as site statistics, user demographics and audience measurement data [9]. These extensions open new possibilities and quality in data mining applications that include bridging business and engineering fields on one hand and business and social sciences on the other hand. A statistics on the anal ...
Introduction to Data Mining
... Group data to form new categories (i.e., clusters), e.g., cluster houses to find distribution patterns ...
... Group data to form new categories (i.e., clusters), e.g., cluster houses to find distribution patterns ...
Web Mining
... tree, i.e. where the leaves are removed. 2.2.2 Clustering With the growth of the World Wide Web it can be very time consuming to analyze every web page on its own. Therefore it is a good idea to cluster web pages based on attributes that can be considered similar to find successful and less successf ...
... tree, i.e. where the leaves are removed. 2.2.2 Clustering With the growth of the World Wide Web it can be very time consuming to analyze every web page on its own. Therefore it is a good idea to cluster web pages based on attributes that can be considered similar to find successful and less successf ...
DSS Chapter 1
... Statistical methods (including both hierarchical and nonhierarchical), such as k-means, k-modes, and so on Neural networks (adaptive resonance theory [ART], self-organizing map [SOM]) Fuzzy logic (e.g., fuzzy c-means algorithm) Genetic algorithms ...
... Statistical methods (including both hierarchical and nonhierarchical), such as k-means, k-modes, and so on Neural networks (adaptive resonance theory [ART], self-organizing map [SOM]) Fuzzy logic (e.g., fuzzy c-means algorithm) Genetic algorithms ...
Data Mining examples
... Reason: data has not been collected for mining it Result: errors and omissions that don’t affect original purpose of data (e.g. age of customer) Typographical errors in nominal attributes values need to be checked for consistency Typographical and measurement errors in numeric attributes ...
... Reason: data has not been collected for mining it Result: errors and omissions that don’t affect original purpose of data (e.g. age of customer) Typographical errors in nominal attributes values need to be checked for consistency Typographical and measurement errors in numeric attributes ...
SAS® Enterprise Miner™ for Desktop
... • Compare models and try multiple approaches and options. Easy-tointerpret displays help users communicate why a particular model is the best predictor. • Validate the accuracy of decision models with holdout data before deploying models with operational systems. • Apply the champion model agains ...
... • Compare models and try multiple approaches and options. Easy-tointerpret displays help users communicate why a particular model is the best predictor. • Validate the accuracy of decision models with holdout data before deploying models with operational systems. • Apply the champion model agains ...
Parallel and Distributed Data Mining: An Introduction
... approximate counts (using hash tables) in the previous level. These counts can be used to rule out many candidates in the current pass that cannot possibly be frequent. The Partition algorithm [8] minimizes I/O by scanning the database only twice. It partitions the database into small chunks which c ...
... approximate counts (using hash tables) in the previous level. These counts can be used to rule out many candidates in the current pass that cannot possibly be frequent. The Partition algorithm [8] minimizes I/O by scanning the database only twice. It partitions the database into small chunks which c ...
Data Profiling with Metanome
... all dependencies, i.e., to answer requests such as “Show me all dependencies of type X that hold in a given dataset.” Metanome is designed for exactly such requests. Therefore, an IT professional needs to specify at least a dataset and the type of dependency that should be discovered to start a prof ...
... all dependencies, i.e., to answer requests such as “Show me all dependencies of type X that hold in a given dataset.” Metanome is designed for exactly such requests. Therefore, an IT professional needs to specify at least a dataset and the type of dependency that should be discovered to start a prof ...
Data Stream Model-Issues, Challenges and Clustering
... existing method or inventing a new one. These methods are as follows. 5.2.1 Sliding window method: In this method the user is more concerned with the current data for analyzing data streams and detailed analysis can be done on current data rather than summarized version of the older data [15]. 5.2.2 ...
... existing method or inventing a new one. These methods are as follows. 5.2.1 Sliding window method: In this method the user is more concerned with the current data for analyzing data streams and detailed analysis can be done on current data rather than summarized version of the older data [15]. 5.2.2 ...
Integrating an Advanced Classifier in WEKA - CEUR
... on the training dataset) for every leaf of the decision tree. These rules are computed by tracing the path from the root of the tree to the specified leaf. Each decision that leads to a leaf is therefore translated into a rule that encapsulates the name of the attribute and the value on which the de ...
... on the training dataset) for every leaf of the decision tree. These rules are computed by tracing the path from the root of the tree to the specified leaf. Each decision that leads to a leaf is therefore translated into a rule that encapsulates the name of the attribute and the value on which the de ...
Article Pdf - Golden Research Thoughts
... Data mining is the process of revealing nontrivial, previously unknown and potentially useful information from large databases [1]. Data analysis plays a vital role in our day to day life. It is the base for investigations in many fields of knowledge, in all fields including science to engineering a ...
... Data mining is the process of revealing nontrivial, previously unknown and potentially useful information from large databases [1]. Data analysis plays a vital role in our day to day life. It is the base for investigations in many fields of knowledge, in all fields including science to engineering a ...
fast algorithm for mining association rules 1
... external and internal information in large databases at low cost. Mining useful information and helpful knowledge from these large databases has thus evolved into an important research area [1–3]. Among them association rule mining has been one of the most popular data-mining subjects, which can be ...
... external and internal information in large databases at low cost. Mining useful information and helpful knowledge from these large databases has thus evolved into an important research area [1–3]. Among them association rule mining has been one of the most popular data-mining subjects, which can be ...
PPT - Mining of Massive Datasets
... J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org ...
... J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org ...
Data Mining
... Compute similarities using the following quantities M01 = the number of attributes where p was 0 and q was 1 M10 = the number of attributes where p was 1 and q was 0 M00 = the number of attributes where p was 0 and q was 0 M11 = the number of attributes where p was 1 and q was 1 ...
... Compute similarities using the following quantities M01 = the number of attributes where p was 0 and q was 1 M10 = the number of attributes where p was 1 and q was 0 M00 = the number of attributes where p was 0 and q was 0 M11 = the number of attributes where p was 1 and q was 1 ...
Cluster analysis
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It will often be necessary to modify data preprocessing and model parameters until the result achieves the desired properties.Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek βότρυς ""grape"") and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals.Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 and Robert Tryon in 1939 and famously used by Cattell beginning in 1943 for trait theory classification in personality psychology.