data mining for a web-based educational system
... propose an algorithm for the discovery of “interesting” association rules within a webbased educational system. The main focus is on mining interesting contrast rules, which are sets of conjunctive rules describing interesting characteristics of different segments within a population. In the context ...
... propose an algorithm for the discovery of “interesting” association rules within a webbased educational system. The main focus is on mining interesting contrast rules, which are sets of conjunctive rules describing interesting characteristics of different segments within a population. In the context ...
Fast Rank-2 Nonnegative Matrix Factorization for
... descent framework is applied to rank-2 NMF, each subproblem requires a solution for nonnegative least squares (NNLS) with only two columns. We design the algorithm for rank2 NMF by exploiting the fact that an exhaustive search for the optimal active set can be performed extremely fast when solving t ...
... descent framework is applied to rank-2 NMF, each subproblem requires a solution for nonnegative least squares (NNLS) with only two columns. We design the algorithm for rank2 NMF by exploiting the fact that an exhaustive search for the optimal active set can be performed extremely fast when solving t ...
isda.softcomputing.net
... mining measures, support and confidence, have been reformulated to reflect this new mining model. Also, new mining algorithms have been presented for the general temporal association rules in transaction databases such as Progressive Partition Miner (PPM) [8], and Segmented Progressive Filter (SPF) [1 ...
... mining measures, support and confidence, have been reformulated to reflect this new mining model. Also, new mining algorithms have been presented for the general temporal association rules in transaction databases such as Progressive Partition Miner (PPM) [8], and Segmented Progressive Filter (SPF) [1 ...
PG Academic Handbook
... These courses will usually cover topics that are not generally covered in the regular courses. Interested students can register for these courses for credits, provided, the above semester-wise credit structure is followed. They are evaluated like any other courses and credits earned count towards de ...
... These courses will usually cover topics that are not generally covered in the regular courses. Interested students can register for these courses for credits, provided, the above semester-wise credit structure is followed. They are evaluated like any other courses and credits earned count towards de ...
Evaluation of clustering methods for adaptive learning systems
... chapter, our main focus is clustering student data (demographic and performance data). The following characterization of typical student data (available in distance learning and adaptive learning systems) is based on a meta-analysis in the previous research (Hämäläinen and Vinni, 2010) and confirmed ...
... chapter, our main focus is clustering student data (demographic and performance data). The following characterization of typical student data (available in distance learning and adaptive learning systems) is based on a meta-analysis in the previous research (Hämäläinen and Vinni, 2010) and confirmed ...
4-ch11ClusAdvanced
... Dimensionality reduction approaches: Construct a much lower dimensional space and search for clusters there (may construct new dimensions by combining some dimensions in the original data) ...
... Dimensionality reduction approaches: Construct a much lower dimensional space and search for clusters there (may construct new dimensions by combining some dimensions in the original data) ...
ABSTRACT Data mining applications have been growing
... advantages of Pro discover include creating bit stream copy of disk to be analyzed while keeping the data in tact without any modifications. Though pro-discover has its own advantages, it suffers from limitations such as it cannot be used efficiently when large amounts of data is considered. Apart f ...
... advantages of Pro discover include creating bit stream copy of disk to be analyzed while keeping the data in tact without any modifications. Though pro-discover has its own advantages, it suffers from limitations such as it cannot be used efficiently when large amounts of data is considered. Apart f ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviors, several data mining based intrusion detection techniques have been applied to networ ...
... continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviors, several data mining based intrusion detection techniques have been applied to networ ...
Toward XML-Based Knowledge Discovery Systems
... Data Item Identity. Given a data item d and its mandatory attributes Name, and Version, the pair hName Versioni uniquely identifies the data item d in the database state. Statement Identity. Given a statement s and its mandatory attribute ID, its value uniquely identifies the statement s in the ...
... Data Item Identity. Given a data item d and its mandatory attributes Name, and Version, the pair hName Versioni uniquely identifies the data item d in the database state. Statement Identity. Given a statement s and its mandatory attribute ID, its value uniquely identifies the statement s in the ...
Multi-query optimization for on
... join. The authors present an approximation algorithm whose output plan’s cost is n times the optimal. The third version is more general since it is a combination of the previous ones. For this case, a greedy algorithm is presented. Exhaustive algorithms are also proposed, but their running time is e ...
... join. The authors present an approximation algorithm whose output plan’s cost is n times the optimal. The third version is more general since it is a combination of the previous ones. For this case, a greedy algorithm is presented. Exhaustive algorithms are also proposed, but their running time is e ...
Spatio-Temporal Data Mining for Typhoon Image Collection
... its performance is, for the better or worse, dependent on the capability of human experts’ 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 re ...
... its performance is, for the better or worse, dependent on the capability of human experts’ 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 re ...
Chap 7: Introduction to Spatial Data Mining
... Classical method: logistic regression, decision trees, bayesian classifier assumes learning samples are independent of each other Spatial auto-correlation violates this assumption! Q? What will a map look like where the properties of a pixel was independent of the properties of other pixels? (see be ...
... Classical method: logistic regression, decision trees, bayesian classifier assumes learning samples are independent of each other Spatial auto-correlation violates this assumption! Q? What will a map look like where the properties of a pixel was independent of the properties of other pixels? (see be ...
Introduction to Spatial Data Mining
... Classical method: logistic regression, decision trees, bayesian classifier assumes learning samples are independent of each other Spatial auto-correlation violates this assumption! Q? What will a map look like where the properties of a pixel was independent of the properties of other pixels? (see be ...
... Classical method: logistic regression, decision trees, bayesian classifier assumes learning samples are independent of each other Spatial auto-correlation violates this assumption! Q? What will a map look like where the properties of a pixel was independent of the properties of other pixels? (see be ...
Spatial Databases A Tour, Chapter 7
... Classical method: logistic regression, decision trees, bayesian classifier assumes learning samples are independent of each other Spatial auto-correlation violates this assumption! Q? What will a map look like where the properties of a pixel was independent of the properties of other pixels? (see be ...
... Classical method: logistic regression, decision trees, bayesian classifier assumes learning samples are independent of each other Spatial auto-correlation violates this assumption! Q? What will a map look like where the properties of a pixel was independent of the properties of other pixels? (see be ...
Financial Frauds: Data Mining based Detection
... this intensifying usage also invites criminals to fraudulently use credit cards to earn money / acquire product or service by unethical means. According to the Nilson Report [16], fraud losses on credit cards, debit cards, and prepaid cards worldwide hit $16.31 billion in 2014 on a total card sales ...
... this intensifying usage also invites criminals to fraudulently use credit cards to earn money / acquire product or service by unethical means. According to the Nilson Report [16], fraud losses on credit cards, debit cards, and prepaid cards worldwide hit $16.31 billion in 2014 on a total card sales ...
A Framework for Monitoring Classifiers` Performance
... Unfortunately, this task makes several fundamental assumptions, namely the “stationary distribution assumption” [24] in the machine learning literature and “non-biased distribution assumption” [28] in the data mining community. Definition 1. The Stationary or Non-Biased Distribution Assumption [24] ...
... Unfortunately, this task makes several fundamental assumptions, namely the “stationary distribution assumption” [24] in the machine learning literature and “non-biased distribution assumption” [28] in the data mining community. Definition 1. The Stationary or Non-Biased Distribution Assumption [24] ...
Dangerous Minds: The Art of Guerrilla Data Mining
... In information security: Not only in “hacking” systems The more information you have, you’ll have a better chance to protect you organization Drafting good policies and procedures as well as picking the correct tools and techniques based on the information that you have. ...
... In information security: Not only in “hacking” systems The more information you have, you’ll have a better chance to protect you organization Drafting good policies and procedures as well as picking the correct tools and techniques based on the information that you have. ...
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.