Data Warehouse and OLAP Technology: An Overview
... “Then, what exactly is a data warehouse?” Data warehouses have been defined in many ways, making it difficult to formulate a rigorous definition. Loosely speaking, a data warehouse refers to a database that is maintained separately from an organization’s operational databases. Data warehouse systems al ...
... “Then, what exactly is a data warehouse?” Data warehouses have been defined in many ways, making it difficult to formulate a rigorous definition. Loosely speaking, a data warehouse refers to a database that is maintained separately from an organization’s operational databases. Data warehouse systems al ...
A Data Mining Technique to Find Optimal Customers for Beneficial
... mining ARM technique to mine the customer information from this database. We then present an artificial intelligence PSO technique to provide an offer to the selected customers. This offer does not affect the company revenues as well as satisfies the customers. This process will make a best relation ...
... mining ARM technique to mine the customer information from this database. We then present an artificial intelligence PSO technique to provide an offer to the selected customers. This offer does not affect the company revenues as well as satisfies the customers. This process will make a best relation ...
A Two-Phase Algorithm for Mining Sequential Patterns with
... In this setting where it is important to capture the sequentiality of the real events in the data, we study the mining of two types of sequential patterns: substring and prefix patterns. The process of mining substring patterns is extensively used in several applications such as biological sequence ...
... In this setting where it is important to capture the sequentiality of the real events in the data, we study the mining of two types of sequential patterns: substring and prefix patterns. The process of mining substring patterns is extensively used in several applications such as biological sequence ...
Mining Top-k Covering Rule Groups for Gene
... Recent studies have shown that such association rules themselves are very useful in the analysis of gene expression data. Due to their relative simplicity, they can be easily interpreted by biologists, providing great help in the search for gene predictors (especially those still unknown to biologis ...
... Recent studies have shown that such association rules themselves are very useful in the analysis of gene expression data. Due to their relative simplicity, they can be easily interpreted by biologists, providing great help in the search for gene predictors (especially those still unknown to biologis ...
MCA syllabus - Jodhpur National University
... Graphs and their application, sequential and linked representation of graph – adjacency matrix, operations on graph, traversing a graph, Dijkstra’s algorithm for shortest distance, DFS and BFS. UNIT V Sorting : Sorting, different sorting techniques such as selection sort, heap sort, bubble sort, qui ...
... Graphs and their application, sequential and linked representation of graph – adjacency matrix, operations on graph, traversing a graph, Dijkstra’s algorithm for shortest distance, DFS and BFS. UNIT V Sorting : Sorting, different sorting techniques such as selection sort, heap sort, bubble sort, qui ...
A general perspective of Big Data: applications, tools, challenges
... text, as well as traditional structured data. – Value (the era of cost associated with data) While the data are being generated, collected and analyzed from different quarters, it is important to state that today’s data have some costs. The data itself can be a “commodity” that can be sold to third ...
... text, as well as traditional structured data. – Value (the era of cost associated with data) While the data are being generated, collected and analyzed from different quarters, it is important to state that today’s data have some costs. The data itself can be a “commodity” that can be sold to third ...
PPT
... distribution with parameter θ The probability density function of the parametric distribution f(x, θ) gives the probability that object x is generated by the distribution The smaller this value, the more likely x is an outlier Non-parametric method Not assume an a-priori statistical model and ...
... distribution with parameter θ The probability density function of the parametric distribution f(x, θ) gives the probability that object x is generated by the distribution The smaller this value, the more likely x is an outlier Non-parametric method Not assume an a-priori statistical model and ...
CoDA: Interactive Cluster Based Concept Discovery
... In today’s applications such as life sciences, e-commerce and sensor networks large amounts of data have to be administrated in databases. With growing size it becomes virtually impossible to manually keep an overview over the data. One way to solve this problem is to semantically structure the data ...
... In today’s applications such as life sciences, e-commerce and sensor networks large amounts of data have to be administrated in databases. With growing size it becomes virtually impossible to manually keep an overview over the data. One way to solve this problem is to semantically structure the data ...
NPClu: A Methodology for Clustering Non
... The goal is to assign these rectangles to a number of clusters. The problem can be formally defined as follows: Given a data set of n non-point objects, find a partitioning of it into groups (clusters) with respect to some similarity measure or distance metric. In general terms, the goal is the memb ...
... The goal is to assign these rectangles to a number of clusters. The problem can be formally defined as follows: Given a data set of n non-point objects, find a partitioning of it into groups (clusters) with respect to some similarity measure or distance metric. In general terms, the goal is the memb ...
Lecture Notes
... Data mining refers to extracting or mining" knowledge from large amounts of data. There are many other terms related to data mining, such as knowledge mining, knowledge extraction, data/pattern analysis, data archaeology, and data dredging. Many people treat data mining as a synonym for another popu ...
... Data mining refers to extracting or mining" knowledge from large amounts of data. There are many other terms related to data mining, such as knowledge mining, knowledge extraction, data/pattern analysis, data archaeology, and data dredging. Many people treat data mining as a synonym for another popu ...
Collinearity: a review of methods to deal with it and a simulation
... collinearity with standard multiple regression and machine-learning approaches. We assessed the performance of each approach by testing its impact on prediction to new data. In the extreme, we tested whether the methods were able to identify the true underlying relationship in a training dataset wit ...
... collinearity with standard multiple regression and machine-learning approaches. We assessed the performance of each approach by testing its impact on prediction to new data. In the extreme, we tested whether the methods were able to identify the true underlying relationship in a training dataset wit ...
guideline on the use of statistical signal detection methods in the
... ‘any unfavorable and unintended sign (including an abnormal laboratory finding, for example), symptom, or disease temporally associated with the use of a medicinal product, whether or not considered related to the medicinal product’. This is to stress that a potential causal relationship between a d ...
... ‘any unfavorable and unintended sign (including an abnormal laboratory finding, for example), symptom, or disease temporally associated with the use of a medicinal product, whether or not considered related to the medicinal product’. This is to stress that a potential causal relationship between a d ...
PPT
... Instead of matching each transaction against every candidate, match it against candidates contained in the hashed buckets ...
... Instead of matching each transaction against every candidate, match it against candidates contained in the hashed buckets ...
Reading: Chapter 5 of Tan et al. 2005
... Direct methods partition the attribute space into smaller subspaces so that all the records that belong to a subspace can be classified using a single classification rule. Indirect methods use the classification rules to provide a succinct description of more complex classification models. Detailed ...
... Direct methods partition the attribute space into smaller subspaces so that all the records that belong to a subspace can be classified using a single classification rule. Indirect methods use the classification rules to provide a succinct description of more complex classification models. Detailed ...
For Review Only - Universidad de Granada
... the generated models will be able to make decisions. In this sense, preprocessing techniques are necessary in the KDD process before applying data mining techniques. Data mining techniques are commonly categorized as descriptive or predictive methods. The former type is devoted to discover interesti ...
... the generated models will be able to make decisions. In this sense, preprocessing techniques are necessary in the KDD process before applying data mining techniques. Data mining techniques are commonly categorized as descriptive or predictive methods. The former type is devoted to discover interesti ...
6 Association Analysis: Basic Concepts and Algorithms
... support count for {Milk, Diapers, Beer} is 2 and the total number of transactions is 5, the rule’s support is 2/5 = 0.4. The rule’s confidence is obtained by dividing the support count for {Milk, Diapers, Beer} by the support count for {Milk, Diapers}. Since there are 3 transactions that contain milk ...
... support count for {Milk, Diapers, Beer} is 2 and the total number of transactions is 5, the rule’s support is 2/5 = 0.4. The rule’s confidence is obtained by dividing the support count for {Milk, Diapers, Beer} by the support count for {Milk, Diapers}. Since there are 3 transactions that contain milk ...
A Survey on Algorithms for Mining Frequent Itemsets over Data
... the approximation of the mining result leads to a dilemma. The smaller the value of ǫ, the more accurate is the approximation but the greater is the number of sub-FIs generated, which requires both more memory space and more CPU processing power. However, if ǫ approaches σ, more false-positive answe ...
... the approximation of the mining result leads to a dilemma. The smaller the value of ǫ, the more accurate is the approximation but the greater is the number of sub-FIs generated, which requires both more memory space and more CPU processing power. However, if ǫ approaches σ, more false-positive answe ...
Web Mining for Personalization: A Survey in the Fuzzy Framework
... (FCMdd) and robust fuzzy c-medoids (RFCMdd)—for fuzzy clustering of relational data. The main objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total fuzzy dissimilarity within each cluster is almost minimized. they also showed a c ...
... (FCMdd) and robust fuzzy c-medoids (RFCMdd)—for fuzzy clustering of relational data. The main objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total fuzzy dissimilarity within each cluster is almost minimized. they also showed a c ...
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.