REVIEW ON EDUCATIONAL DATA MINING TECHNIQUES
... respond to educational system and their responses impact their learning. Its objective is to analyze educational data in order to resolve educational research issues. In recent years there is rapid growth in education sector which leads to growing of education data so mining of education data become ...
... respond to educational system and their responses impact their learning. Its objective is to analyze educational data in order to resolve educational research issues. In recent years there is rapid growth in education sector which leads to growing of education data so mining of education data become ...
Social Communities Detection in Social Media
... expected better performance in circle clustering. These are many ways to combine these two methods like to learn a weight to combine the results from node characteristic analysis and the network structure analysis. Then give a final result to decide whether this node or edge belongs to this group or ...
... expected better performance in circle clustering. These are many ways to combine these two methods like to learn a weight to combine the results from node characteristic analysis and the network structure analysis. Then give a final result to decide whether this node or edge belongs to this group or ...
Outlier Ensembles - Outlier Definition, Detection, and Description
... • The feature bagging work discussed in Lazarevic et al may be considered a first formal description of outlier ensemble analysis in a real setting. • Numerous methods were proposed earlier to this work which could be considered ensembles, but were never formally recognized as ensembles in the litera ...
... • The feature bagging work discussed in Lazarevic et al may be considered a first formal description of outlier ensemble analysis in a real setting. • Numerous methods were proposed earlier to this work which could be considered ensembles, but were never formally recognized as ensembles in the litera ...
Development of a Data Warehouse for Cancer Diagnosis
... health care centersare still stand along, they are not communicating with other health care center, and they don’tshare their documents with others. But now in our design health care data warehouse, doctors can also share patient record to others, they can take decision from others. Most of the heal ...
... health care centersare still stand along, they are not communicating with other health care center, and they don’tshare their documents with others. But now in our design health care data warehouse, doctors can also share patient record to others, they can take decision from others. Most of the heal ...
ppt
... between q and d2 is large but, the distribution of terms in the query q and the distribution of terms in the document d2 are very similar This is not what we want! ...
... between q and d2 is large but, the distribution of terms in the query q and the distribution of terms in the document d2 are very similar This is not what we want! ...
Title Distributed Clustering Algorithm for Spatial Data Mining Author(s)
... scales up well in terms of running time, and result quality, we also compared it to two other clustering algorithms BIRCH and CURE and we show clearly this approach is much more efficient than the two algorithms. Keywords— Spatial data, clustering, distributed mining, data analysis, k-means. ...
... scales up well in terms of running time, and result quality, we also compared it to two other clustering algorithms BIRCH and CURE and we show clearly this approach is much more efficient than the two algorithms. Keywords— Spatial data, clustering, distributed mining, data analysis, k-means. ...
Title Distributed Clustering Algorithm for Spatial Data Mining Author(s)
... scales up well in terms of running time, and result quality, we also compared it to two other clustering algorithms BIRCH and CURE and we show clearly this approach is much more efficient than the two algorithms. Keywords— Spatial data, clustering, distributed mining, data analysis, k-means. ...
... scales up well in terms of running time, and result quality, we also compared it to two other clustering algorithms BIRCH and CURE and we show clearly this approach is much more efficient than the two algorithms. Keywords— Spatial data, clustering, distributed mining, data analysis, k-means. ...
design of product placement layout in retail shop using market
... merchandise. The purpose of this paper is to identify associated products, which then grouped in mix merchandise with the use of market basket analysis. This association between products then will be applied in the design layout of the product in the supermarket. The process of identifying the relat ...
... merchandise. The purpose of this paper is to identify associated products, which then grouped in mix merchandise with the use of market basket analysis. This association between products then will be applied in the design layout of the product in the supermarket. The process of identifying the relat ...
software tools for teaching undergraduate data mining course
... the same range of basic functions at their disposal. The representation which MATLAB implements, is dealing with all data in the form of matrices. This allows for many varied algorithmic implementations [4], which, as we shall see, is crucial for any data mining package. Other advantages of MATLAB i ...
... the same range of basic functions at their disposal. The representation which MATLAB implements, is dealing with all data in the form of matrices. This allows for many varied algorithmic implementations [4], which, as we shall see, is crucial for any data mining package. Other advantages of MATLAB i ...
V. Kumar
... A novel clustering technique was developed to identify regions of uniform behavior in spatiotemporal data. The use of clustering for discovering climate indices is driven by the intuition that a climate phenomenon is expected to involve a significant region of the ocean or atmosphere where the behav ...
... A novel clustering technique was developed to identify regions of uniform behavior in spatiotemporal data. The use of clustering for discovering climate indices is driven by the intuition that a climate phenomenon is expected to involve a significant region of the ocean or atmosphere where the behav ...
Result Analysis Using Various Pattern Mining Techniques:
... multiple passes are made over the data. Before each pass, a set of new potentially large sequences called candidate sequences are generated. Two families of algorithms are presented by Agrawal & Srikant (1995) [13] and are referred to as count-all and count-some algorithms. The count-all algorithm f ...
... multiple passes are made over the data. Before each pass, a set of new potentially large sequences called candidate sequences are generated. Two families of algorithms are presented by Agrawal & Srikant (1995) [13] and are referred to as count-all and count-some algorithms. The count-all algorithm f ...
How To Satisfy EU Directive On Data Protection
... While each EU member has it's own data protection law, the Directive serves as a basis for local laws. Our research will therefore enlighten the information privacy problem from the Directive perspective. First of all, there is a question what problems could arouse using data warehouse technology on ...
... While each EU member has it's own data protection law, the Directive serves as a basis for local laws. Our research will therefore enlighten the information privacy problem from the Directive perspective. First of all, there is a question what problems could arouse using data warehouse technology on ...
sampling techniques and knowledge discovery with data mining for
... actionable and ultimately understandable patterns in data. Pattern in the given data is an expression in interpreting the data or a model applicable to the subset in given data. Extracting a pattern designates fitting a model to data, finding structure from data, or in general any high-level descrip ...
... actionable and ultimately understandable patterns in data. Pattern in the given data is an expression in interpreting the data or a model applicable to the subset in given data. Extracting a pattern designates fitting a model to data, finding structure from data, or in general any high-level descrip ...
Customer Data Warehouse - Diuf
... treated (e.g. coding conventions are standardized) – time-variant: data are organized by various time periods (e.g. months) – non-volatile: the database is not uploaded in real time. ...
... treated (e.g. coding conventions are standardized) – time-variant: data are organized by various time periods (e.g. months) – non-volatile: the database is not uploaded in real time. ...
Comparison of Data Preparation Methods for Use in Model Development with SAS® Enterprise Miner
... This was done using EM 4.3 and SAS 9.1. The processes to build two very different models were compared using EM on the raw database and using some of the standard data reductions techniques in base SAS and then applying EM. The first of these models was a propensity to purchase (look alike) model fo ...
... This was done using EM 4.3 and SAS 9.1. The processes to build two very different models were compared using EM on the raw database and using some of the standard data reductions techniques in base SAS and then applying EM. The first of these models was a propensity to purchase (look alike) model fo ...
Introduction
... Log files are discrete recording of user action during the utilization of software (Guzdial, 1993). It offers the possibility to analyze human-computer interaction whether in realtime, so the software can adjust to what have been done by a user or to facilitate and make more precise a feedback given ...
... Log files are discrete recording of user action during the utilization of software (Guzdial, 1993). It offers the possibility to analyze human-computer interaction whether in realtime, so the software can adjust to what have been done by a user or to facilitate and make more precise a feedback given ...
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.