International Conference On Intelligent Computing
... they are not known to begin with. Clustering can be used to generate such labels. The objects are clustered or grouped based on the principle of maximizing the intraclass similarity and minimizing the interclass similarity. That is, clusters of objects are formed so that objects within a cluster hav ...
... they are not known to begin with. Clustering can be used to generate such labels. The objects are clustered or grouped based on the principle of maximizing the intraclass similarity and minimizing the interclass similarity. That is, clusters of objects are formed so that objects within a cluster hav ...
A Short Survey on Applications of Rough Sets Theory in Power
... The proposed methodology was tested on a data base of 417 consumers. The SOM process resulted into 10 clusters, and RS theory was used in order to extract the rules required for classifying the consumers. Authors of [29] did not present in detail the implementation process or the mathematical formal ...
... The proposed methodology was tested on a data base of 417 consumers. The SOM process resulted into 10 clusters, and RS theory was used in order to extract the rules required for classifying the consumers. Authors of [29] did not present in detail the implementation process or the mathematical formal ...
LILOLE—A Framework for Lifelong Learning from Sensor Data
... better with “subject-specific training data”. In order to optimise general activity recognition models for individual users, a number of different approaches have been developed, which try to optimise general models to specific users [23, 35]. The reason for relying on general models, even if they ...
... better with “subject-specific training data”. In order to optimise general activity recognition models for individual users, a number of different approaches have been developed, which try to optimise general models to specific users [23, 35]. The reason for relying on general models, even if they ...
FIT5142 Advanced data mining Unit Guide Semester 2, 2015
... Solving classification, clustering, association rules analysis and regression problems on different kinds of data are covered. Data pre-processing methods for dealing with noisy and missing data in the context of Big Data are reviewed. Evaluation and analysis of data mining models are emphasised. Ha ...
... Solving classification, clustering, association rules analysis and regression problems on different kinds of data are covered. Data pre-processing methods for dealing with noisy and missing data in the context of Big Data are reviewed. Evaluation and analysis of data mining models are emphasised. Ha ...
Analysis of Breast Feeding Data Using Data Mining Methods
... are then divided into distinct classes. For example, in the breast feeding survey data we can use a feature indicating that the mother chooses to breast feed her baby or not as the class variable. The objective of classification is to predict the class variable using descriptive variables automatica ...
... are then divided into distinct classes. For example, in the breast feeding survey data we can use a feature indicating that the mother chooses to breast feed her baby or not as the class variable. The objective of classification is to predict the class variable using descriptive variables automatica ...
A Data Mining Architecture for Clustered Environments
... reports that the system can support the moving of large volumes of mining data. The idea is founded on a theory similar to JAM system. Nevertheless they use a model representation language (PMML) and storage system called Osiris. BODHI [3] is a hierarchical agent based distributed learning system. T ...
... reports that the system can support the moving of large volumes of mining data. The idea is founded on a theory similar to JAM system. Nevertheless they use a model representation language (PMML) and storage system called Osiris. BODHI [3] is a hierarchical agent based distributed learning system. T ...
Uncertain Data Classification Using Decision Tree
... Categorical Uncertain attributes. Marks attribute is a numerical uncertain attribute (NUA) and Result attribute is a categorical uncertain attribute (CUA). Class label can also be either numerical or categorical. 3. PROBLEM DEFINITION In many real life applications information cannot be ideally repr ...
... Categorical Uncertain attributes. Marks attribute is a numerical uncertain attribute (NUA) and Result attribute is a categorical uncertain attribute (CUA). Class label can also be either numerical or categorical. 3. PROBLEM DEFINITION In many real life applications information cannot be ideally repr ...
A Data Clustering Algorithm for Mining Patterns
... categorical attributes, where the domain of an attribute is a finite and unordered set of values [13, 14]. As an example, consider a categorical data set with attributes car-manufacturer, model, type, and color, and data points ('Honda', 'Civic', 'hatchback', 'green') and ('Ford', 'Focus', 'sedan', ...
... categorical attributes, where the domain of an attribute is a finite and unordered set of values [13, 14]. As an example, consider a categorical data set with attributes car-manufacturer, model, type, and color, and data points ('Honda', 'Civic', 'hatchback', 'green') and ('Ford', 'Focus', 'sedan', ...
Data Mining and Hotspot Detection in an Urban Development Project
... Chamont Wang1 and Pin-Shuo Liu2 1 The College of New Jersey and 2 William Paterson University Abstract: Modern statistical analysis often involves large amount of data from many application areas with diverse data types and complicated data structures. This paper gives a brief survey of certain larg ...
... Chamont Wang1 and Pin-Shuo Liu2 1 The College of New Jersey and 2 William Paterson University Abstract: Modern statistical analysis often involves large amount of data from many application areas with diverse data types and complicated data structures. This paper gives a brief survey of certain larg ...
a few useful things to Know about machine Learning
... according to which no learner can beat random guessing over all possible functions to be learned.25 This seems like rather depressing news. How then can we ever hope to learn anything? Luckily, the functions we want to learn in the real world are not drawn uniformly from the set of all mathematicall ...
... according to which no learner can beat random guessing over all possible functions to be learned.25 This seems like rather depressing news. How then can we ever hope to learn anything? Luckily, the functions we want to learn in the real world are not drawn uniformly from the set of all mathematicall ...
Improving the Classification Accuracy with Ensemble of
... purpose, there have been different established classifiers which are reported in the literature from time to time with extensions. In this paper, it is attempted to claim that an individual classifier may not be able to predict the class of an unknown pattern correctly. On the other hand, if the mul ...
... purpose, there have been different established classifiers which are reported in the literature from time to time with extensions. In this paper, it is attempted to claim that an individual classifier may not be able to predict the class of an unknown pattern correctly. On the other hand, if the mul ...
Privacy-Preserving Data Visualization using Parallel Coordinates
... (Figure 1b). Imposing privacy-preserving constraints in the screen-space, rather than the data space, meets these criteria. We demonstrate this idea in this paper through a PPDV technique based on screen-space clustering in parallel coordinates. ...
... (Figure 1b). Imposing privacy-preserving constraints in the screen-space, rather than the data space, meets these criteria. We demonstrate this idea in this paper through a PPDV technique based on screen-space clustering in parallel coordinates. ...
An Architecture for Privacy-preserving Mining of Client Information
... the sites collecting the original information. We assume that these parties are non-colluding and semihonest.( For a detailed description of the semi-honest model, refer to (Goldreich 2000)) In effect, all parties correctly follow the protocols, but then are free to use whatever information they see ...
... the sites collecting the original information. We assume that these parties are non-colluding and semihonest.( For a detailed description of the semi-honest model, refer to (Goldreich 2000)) In effect, all parties correctly follow the protocols, but then are free to use whatever information they see ...
What is Data Mining ?
... Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns Maximizing intra-class similarity & minimizing interclass similarity Outlier: Data object that does not comply with the general behavior of the data Noise or exception? ...
... Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns Maximizing intra-class similarity & minimizing interclass similarity Outlier: Data object that does not comply with the general behavior of the data Noise or exception? ...
A Review of KDD-Data Mining Framework and Its Application in
... Management also defined logistics as the process of planning, implementing and controlling the efficient, cost effective flow and storage of raw materials, in-process inventory, finished goods and related information from point of origin to point of consumption for the purpose of conforming to custo ...
... Management also defined logistics as the process of planning, implementing and controlling the efficient, cost effective flow and storage of raw materials, in-process inventory, finished goods and related information from point of origin to point of consumption for the purpose of conforming to custo ...
Chapter 5
... 4. Expert systems (ES) • Encapsulates knowledge in form of “If/Then” rules – If Patient_Temp > 103, Then start High_Fever_Procedure ...
... 4. Expert systems (ES) • Encapsulates knowledge in form of “If/Then” rules – If Patient_Temp > 103, Then start High_Fever_Procedure ...
Insights to Existing Techniques of Subspace Clustering in High
... the area of datamining, such issues are continuously considered as critical problems where the probable solution lies in cluster analysis [2]. In easier manner, it can be said that cluster analysis assist in making the messy data to a meaningful data that will be easy to analyse. It attempts to disc ...
... the area of datamining, such issues are continuously considered as critical problems where the probable solution lies in cluster analysis [2]. In easier manner, it can be said that cluster analysis assist in making the messy data to a meaningful data that will be easy to analyse. It attempts to disc ...
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.