CIS732-Lecture-27-20080402 - Kansas State University
... needs ML/MD processing Analysis requirements Multi-dimensional trends and unusual patterns Capturing important changes at multi-dimensions/levels Fast, real-time detection and response Comparing with data cube: Similarity and differences ...
... needs ML/MD processing Analysis requirements Multi-dimensional trends and unusual patterns Capturing important changes at multi-dimensions/levels Fast, real-time detection and response Comparing with data cube: Similarity and differences ...
Mining TOP-K Strongly Correlated Pairs in Large Databases
... cube computations. He showed that finding the subcubes that satisfy statistical tests such as χ2 are inherently NPhard, but can be made more tractable using approximation schemes. Jermaine [9] also presented an iterative procedure for high-dimensional correlation analysis by shaving off part of the ...
... cube computations. He showed that finding the subcubes that satisfy statistical tests such as χ2 are inherently NPhard, but can be made more tractable using approximation schemes. Jermaine [9] also presented an iterative procedure for high-dimensional correlation analysis by shaving off part of the ...
Syllabus The German Credit Data
... May be only a few would do. For example, you could try just having attributes 2,3,5,7,10,17 and 21. Try out some combinations.(You had removed two attributes in problem 7. Remember to reload the arff data file to get all the attributes initially before you start selecting the ones you want.) 9. Some ...
... May be only a few would do. For example, you could try just having attributes 2,3,5,7,10,17 and 21. Try out some combinations.(You had removed two attributes in problem 7. Remember to reload the arff data file to get all the attributes initially before you start selecting the ones you want.) 9. Some ...
shekhar07
... Spatial Autocorrelation (SA) First Law of Geography “All things are related, but nearby things are more related than distant things. [Tobler, 1970]” ...
... Spatial Autocorrelation (SA) First Law of Geography “All things are related, but nearby things are more related than distant things. [Tobler, 1970]” ...
as a PDF
... way of the well-known Apriori algorithm [2]. It is traversing iteratively the set of all itemsets in a levelwise manner. During each iteration one level is considered: a subset of candidate itemsets is created by joining the frequent itemsets discovered during the previous iteration, the supports of ...
... way of the well-known Apriori algorithm [2]. It is traversing iteratively the set of all itemsets in a levelwise manner. During each iteration one level is considered: a subset of candidate itemsets is created by joining the frequent itemsets discovered during the previous iteration, the supports of ...
Collinearity: a review of methods to deal with it and a simulation
... Collinearity describes the situation where two or more predictor variables in a statistical model are linearly related (sometimes also called multicollinearity: Alin 2010). Many statistical routines, notably those most commonly used in ecology, are sensitive to collinearity (Belsley 1991, Chatfield ...
... Collinearity describes the situation where two or more predictor variables in a statistical model are linearly related (sometimes also called multicollinearity: Alin 2010). Many statistical routines, notably those most commonly used in ecology, are sensitive to collinearity (Belsley 1991, Chatfield ...
From Local Patterns to Global Models: The LeGo Approach to Data
... of local patterns in supervised settings, with the objective function—typically a rule interestingness measure—being a parameter of the task itself. A large variety of measures suitable for this task have been investigated [45], many of which are well-known heuristics for inductive rule learning [14 ...
... of local patterns in supervised settings, with the objective function—typically a rule interestingness measure—being a parameter of the task itself. A large variety of measures suitable for this task have been investigated [45], many of which are well-known heuristics for inductive rule learning [14 ...
What is data mining
... – Goal: subdivide a market into distinct subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix. – Approach: • Collect different attributes of customers based on their geographical and lifestyle related information. • Find clu ...
... – Goal: subdivide a market into distinct subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix. – Approach: • Collect different attributes of customers based on their geographical and lifestyle related information. • Find clu ...
Application of computational intelligence in modeling and
... HVAC (Heating Ventilating and Air-Conditioning) system is multivariate, nonlinear, and shares time-varying characteristics. It poses challenges for both system modeling and performance optimization. Traditional modeling approaches based on mathematical equations limit the nature of the optimization ...
... HVAC (Heating Ventilating and Air-Conditioning) system is multivariate, nonlinear, and shares time-varying characteristics. It poses challenges for both system modeling and performance optimization. Traditional modeling approaches based on mathematical equations limit the nature of the optimization ...
Mining Frequent Patterns Without Candidate Generation
... Construct models (functions) that describe and distinguish classes or concepts for future prediction ...
... Construct models (functions) that describe and distinguish classes or concepts for future prediction ...
Big data preprocessing: Methods and Prospects
... of noise data is mandatory [22]. In supervised problems, noise can affect the input features, the output values or both. When noise is present in the input attributes, it is usually referred as attribute noise. The worse case is when the noise affects the output attribute, as this means that the bia ...
... of noise data is mandatory [22]. In supervised problems, noise can affect the input features, the output values or both. When noise is present in the input attributes, it is usually referred as attribute noise. The worse case is when the noise affects the output attribute, as this means that the bia ...
Mining Scientific Data: Past, Present, and Future
... Officials in Indonesia say illegal burning to clear land has caused rampant wildfires across Borneo and Sumatra ... eight million hectares have gone up in smoke over the last month, and fires are still burning out of control on the island of Borneo. SDM – April 2010 ...
... Officials in Indonesia say illegal burning to clear land has caused rampant wildfires across Borneo and Sumatra ... eight million hectares have gone up in smoke over the last month, and fires are still burning out of control on the island of Borneo. SDM – April 2010 ...
Spatio-Temporal Data Mining for Typhoon Image Collection
... pattern recognition, which is subjective in nature. The above arguments remind us of a similar framework in the informatics community such as content-based image retrieval and case-based learning, or we may reach more principled understanding of the Dvorak method in the framework of pattern recognit ...
... pattern recognition, which is subjective in nature. The above arguments remind us of a similar framework in the informatics community such as content-based image retrieval and case-based learning, or we may reach more principled understanding of the Dvorak method in the framework of pattern recognit ...
Case Studies in Data Mining
... 3. Evaluation of performance for association rules ..................................................... 4. Performance of association rules - Simpson's paradox ............................................ 11. Clustering 1 ............................................................................. ...
... 3. Evaluation of performance for association rules ..................................................... 4. Performance of association rules - Simpson's paradox ............................................ 11. Clustering 1 ............................................................................. ...
Web Usage Mining: Application To An Online Educational Digital
... http://ia.usu.edu) utilized the system in order to find online learning resources, place them in a new online instructional activities, and share and use them with students. The online learning resources can be found in educational digital libraries such as the National Science Digital Library (NSLD ...
... http://ia.usu.edu) utilized the system in order to find online learning resources, place them in a new online instructional activities, and share and use them with students. The online learning resources can be found in educational digital libraries such as the National Science Digital Library (NSLD ...
A survey on Data Mining: Tools, Techniques, Applications, Trends
... emerging as a new, fundamental research area with important applications to science, engineering, medicine, business, and education. Data mining attempts to formulate analyze and implement basic induction processes that facilitate the extraction of meaningful information and knowledge from unstructu ...
... emerging as a new, fundamental research area with important applications to science, engineering, medicine, business, and education. Data mining attempts to formulate analyze and implement basic induction processes that facilitate the extraction of meaningful information and knowledge from unstructu ...
High Performance Mining of Maximal Frequent Itemsets
... reduce the search time and the number of subset testing operations. Since it is not to be expected that one single approach will be suitable for all types of data, we analyze the behaviour of algorithms Mafia, Genmax and Fpmax, under various types of data. We validate our analysis through careful ex ...
... reduce the search time and the number of subset testing operations. Since it is not to be expected that one single approach will be suitable for all types of data, we analyze the behaviour of algorithms Mafia, Genmax and Fpmax, under various types of data. We validate our analysis through careful ex ...
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.