Data Mining Jargon
... Data Management – all of the tasks required to manage data such as correcting data entry errors, estimating values of missing data, subsetting or combining sets of data. Data Mart – A small data warehouse that is focused on a single area such as a research project or a single department such as sal ...
... Data Management – all of the tasks required to manage data such as correcting data entry errors, estimating values of missing data, subsetting or combining sets of data. Data Mart – A small data warehouse that is focused on a single area such as a research project or a single department such as sal ...
Using Genetic Algorithms To Find Temporal Patterns Indicative Of
... function represents the value of future “eventness” for the current time index. It is, to use an analogy, a measure of how much gold is at the end of the rainbow (temporal pattern). The event characterization function is defined such that its value at t correlates highly with the occurrence of an ev ...
... function represents the value of future “eventness” for the current time index. It is, to use an analogy, a measure of how much gold is at the end of the rainbow (temporal pattern). The event characterization function is defined such that its value at t correlates highly with the occurrence of an ev ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... In the data mining, the data’s quality directly impacts on the accuracy of the extracted user features and the derived rules. In data preprocess module, the data packet will be converted into suitable mining forms by using many techniques such as data cleaning, data integration, data reduction techn ...
... In the data mining, the data’s quality directly impacts on the accuracy of the extracted user features and the derived rules. In data preprocess module, the data packet will be converted into suitable mining forms by using many techniques such as data cleaning, data integration, data reduction techn ...
Berger, Charlie. "Oracle Data Mining 11g Release 2: Competing on
... representative attributes. Similar in high level concept to Principal Components Analysis (PCA), but able to handle much larger amounts of attributes and create new features in an additive nature, NMF is a powerful, cutting-edge data mining algorithm that can be used for a variety of use cases. NMF ...
... representative attributes. Similar in high level concept to Principal Components Analysis (PCA), but able to handle much larger amounts of attributes and create new features in an additive nature, NMF is a powerful, cutting-edge data mining algorithm that can be used for a variety of use cases. NMF ...
Outlier Detection using Improved Genetic K-means
... Data mining, in general, deals with the discovery of nontrivial, hidden and interesting knowledge from different types of data. With the development of information technologies, the number of databases, as well as their dimension and complexity, grow rapidly. It is necessary what we need automated a ...
... Data mining, in general, deals with the discovery of nontrivial, hidden and interesting knowledge from different types of data. With the development of information technologies, the number of databases, as well as their dimension and complexity, grow rapidly. It is necessary what we need automated a ...
Mamitsuka, H. and Abe, N., Efficient Mining from Large Databases
... of query by committee (Seung et al., 1992) and `bagging' (Breiman, 1996). The basic idea is that query points are chosen by picking points on which the predictions made by the hypotheses resulting from applying the component inductive algorithm to sub-samples obtained via re-sampling from the origin ...
... of query by committee (Seung et al., 1992) and `bagging' (Breiman, 1996). The basic idea is that query points are chosen by picking points on which the predictions made by the hypotheses resulting from applying the component inductive algorithm to sub-samples obtained via re-sampling from the origin ...
Interpreter of Maladies: Redescription Mining Applied to Biomedical
... about a given subset of data. The goal of redescription mining is to find subsets of data that afford multiple descriptions. By filtering, evaluating, and cross-correlating these multiple redescriptions, we may be able to uncover the core biology of a disease. Other methods provide similar approache ...
... about a given subset of data. The goal of redescription mining is to find subsets of data that afford multiple descriptions. By filtering, evaluating, and cross-correlating these multiple redescriptions, we may be able to uncover the core biology of a disease. Other methods provide similar approache ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... Chao-Wei Li et. al [10] proposed SA-Miner algorithm that discovers frequent itemsets through support approximation. SA-Miner learns a concept by constructing models for the support relationships that describe the concept. The proposed method not only performs efficiently in terms of time and memory ...
... Chao-Wei Li et. al [10] proposed SA-Miner algorithm that discovers frequent itemsets through support approximation. SA-Miner learns a concept by constructing models for the support relationships that describe the concept. The proposed method not only performs efficiently in terms of time and memory ...
Course outline
... An extension will be granted only if there is a need and when requested several days in advance. ...
... An extension will be granted only if there is a need and when requested several days in advance. ...
Combining Information Visualization with Data Mining
... computer enables rapid display of large data sets with rich user control panels to support exploration (Card, Mackinlay and Shneiderman, 1999). Users can manipulate up to a million data items with 100- millisecond update of displays that present color-coded, size-coded markers for each item. With th ...
... computer enables rapid display of large data sets with rich user control panels to support exploration (Card, Mackinlay and Shneiderman, 1999). Users can manipulate up to a million data items with 100- millisecond update of displays that present color-coded, size-coded markers for each item. With th ...
High Performance Distributed Systems for Data Mining
... Recently, a framework for such applications on Grid platforms has been proposed as the Knowledge Grid (K-Grid) [16]. The K-Grid is a middleware for distributed KDD. It is composed by two layers. At the bottom there is the layer of core services, implemented over standard grid middleware, like Globus ...
... Recently, a framework for such applications on Grid platforms has been proposed as the Knowledge Grid (K-Grid) [16]. The K-Grid is a middleware for distributed KDD. It is composed by two layers. At the bottom there is the layer of core services, implemented over standard grid middleware, like Globus ...
Detection of Outliers - Department of Science and Technology
... a point to all its 1NN, 2NN, ..., kNN as an outlier score. ...
... a point to all its 1NN, 2NN, ..., kNN as an outlier score. ...
IR and Social Media
... • Content Preparation: translating the data in to an internal format; enriching the data. • Content Storage: preserving that data in a manner that allows for access via an API. • Mining/Applications ...
... • Content Preparation: translating the data in to an internal format; enriching the data. • Content Storage: preserving that data in a manner that allows for access via an API. • Mining/Applications ...
Customer Retention Predictive Modeling in HealthCare Insurance Industry
... Before any data mining effort can begin, data must be extracted and prepared in a way to maximize the value of the available data and improve the results of complex statistical analysis. The goal is to bring together all of the disparate data in an organization and transform it into meaningful, usef ...
... Before any data mining effort can begin, data must be extracted and prepared in a way to maximize the value of the available data and improve the results of complex statistical analysis. The goal is to bring together all of the disparate data in an organization and transform it into meaningful, usef ...
Ant Clustering Algorithm - Intelligent Information Systems
... performance when compared to other algprithms. we therefore at the beginning choose the k-means algorithm. In our experiments, we run k-means algorithm using the correct cluster number k. We have applied ACA to real world databases from the Machine Learning repository which are often used as benchma ...
... performance when compared to other algprithms. we therefore at the beginning choose the k-means algorithm. In our experiments, we run k-means algorithm using the correct cluster number k. We have applied ACA to real world databases from the Machine Learning repository which are often used as benchma ...
Document
... Business Case The client has internally developed BI component strategically positioned in the BI ecosystem. Cas Apanowicz of IT Horizon Corp. was retained to evaluate the solution. The Data Lake approach was recommended resulting in total saving of $778,000 and shortening the implementation time f ...
... Business Case The client has internally developed BI component strategically positioned in the BI ecosystem. Cas Apanowicz of IT Horizon Corp. was retained to evaluate the solution. The Data Lake approach was recommended resulting in total saving of $778,000 and shortening the implementation time f ...
Change Detection in Data Streams by Testing Exchangeability
... strangeness of a point, the less likely it comes from the model Condition for a valid strangeness measure: A strangeness value of a data point at a particular time instance should be independent of the order it is observed with respect to the other data points. ...
... strangeness of a point, the less likely it comes from the model Condition for a valid strangeness measure: A strangeness value of a data point at a particular time instance should be independent of the order it is observed with respect to the other data points. ...
ICAIT1519
... In this section; we describe the results of applying the data mining techniques to the data in our data set, for each of the four data mining tasks; Association, classification, clustering and outlier detection, and how we can benefited from the discovered knowledge. We have used SPSS (Statistical P ...
... In this section; we describe the results of applying the data mining techniques to the data in our data set, for each of the four data mining tasks; Association, classification, clustering and outlier detection, and how we can benefited from the discovered knowledge. We have used SPSS (Statistical P ...
Development of a Data Mining Tool on Android Smartphones
... Data mining is widely used in diverse areas. There is a great deal of research in data mining systems available today and yet there are many challenges in this field. For example, an education system [7] that mined in educational environment is called Educational Data Mining. This research used K-me ...
... Data mining is widely used in diverse areas. There is a great deal of research in data mining systems available today and yet there are many challenges in this field. For example, an education system [7] that mined in educational environment is called Educational Data Mining. This research used K-me ...
DATA MINING
... Models Predictive: predict about values of data Descriptive: identify patterns/relationships in data [explore the properties of data] ...
... Models Predictive: predict about values of data Descriptive: identify patterns/relationships in data [explore the properties of data] ...
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.