A Complexity-Invariant Distance Measure for Time Series
... Figure 2: left) If compared before amplitude scaling, these two time series appear very different. right) When matching a heartbeat query against a stream, the first two match well, but subsequently the drifting offset means the remaining heartbeats are not discovered ...
... Figure 2: left) If compared before amplitude scaling, these two time series appear very different. right) When matching a heartbeat query against a stream, the first two match well, but subsequently the drifting offset means the remaining heartbeats are not discovered ...
Chapter 23 Mining for Complex Models Comprising Feature
... In our exploration of the datasets we have tested a variety of methods, which implement different ways of cost function minimization. This broadens the search area in the model space. Final models for the five datasets were based on Support Vector Machines, Normalized Radial Basis Functions and Near ...
... In our exploration of the datasets we have tested a variety of methods, which implement different ways of cost function minimization. This broadens the search area in the model space. Final models for the five datasets were based on Support Vector Machines, Normalized Radial Basis Functions and Near ...
An Effective Data Preprocessing Technique for Improved Data
... operation, like data transformation by transformation agent, data reduction by discretization agent, data cleaning by clean miss and clean noisy agent. The functions of the preprocessing software include data integration, data reduction, data transformation, data cleaning and data visualization. Eac ...
... operation, like data transformation by transformation agent, data reduction by discretization agent, data cleaning by clean miss and clean noisy agent. The functions of the preprocessing software include data integration, data reduction, data transformation, data cleaning and data visualization. Eac ...
Survey on Spatio-Temporal Clustering
... Clustering is one of the major data mining methods in large databases for knowledge discovery. It is the process of grouping large data sets according to their similarity. Spatial data records information regarding the location, shape and its effect on features (e.g. geographical features). When suc ...
... Clustering is one of the major data mining methods in large databases for knowledge discovery. It is the process of grouping large data sets according to their similarity. Spatial data records information regarding the location, shape and its effect on features (e.g. geographical features). When suc ...
Integration of Data Mining and Relational Databases
... particular, cases are far better represented as nested records than flat records1. Traditional SQL representation also falls short is in capturing metadata on columns. To effectively derive and use a mining model, we must be able to identify properties of an attribute (e.g., discrete vs. continuous) ...
... particular, cases are far better represented as nested records than flat records1. Traditional SQL representation also falls short is in capturing metadata on columns. To effectively derive and use a mining model, we must be able to identify properties of an attribute (e.g., discrete vs. continuous) ...
A Survey on Pre-processing and Post-processing
... learning approach, it is essential either replace the missing values with some appropriate values or to remove the instances having missing values [4]. Missing values are handled either by replacing it with other values or by removing the incomplete instance. Some ML algorithm such as C4.5 Decision ...
... learning approach, it is essential either replace the missing values with some appropriate values or to remove the instances having missing values [4]. Missing values are handled either by replacing it with other values or by removing the incomplete instance. Some ML algorithm such as C4.5 Decision ...
Mining Partial Periodicity in Large Time Series Databases using
... The notation to be used in this paper to describe partially periodic patterns is similar to that used in [11]. We describe a pattern by listing the events within the pattern in the order in which they occur, substituting an asterisk (*) for don’t care events (ie. any event within the subset of defin ...
... The notation to be used in this paper to describe partially periodic patterns is similar to that used in [11]. We describe a pattern by listing the events within the pattern in the order in which they occur, substituting an asterisk (*) for don’t care events (ie. any event within the subset of defin ...
Quran question and answer corpus for data mining with WEKA
... important issue; and low accuracies or wrong answers are not acceptable in the religious field especially in the domain of the holy Quran. Since there are not enough existing resources specifically designed for Quran questions and answers, we propose to merge different data subsets to comprise the Q ...
... important issue; and low accuracies or wrong answers are not acceptable in the religious field especially in the domain of the holy Quran. Since there are not enough existing resources specifically designed for Quran questions and answers, we propose to merge different data subsets to comprise the Q ...
Feature Selection Algorithm with Discretization and PSO
... Discretization of continuous attributes is an important technique for the pre-processing task in the classification problems with simplification analysis, and it has played significant role in the machine learning algorithms [1][2][3][4][5]. However, learning process from continuous attributes to di ...
... Discretization of continuous attributes is an important technique for the pre-processing task in the classification problems with simplification analysis, and it has played significant role in the machine learning algorithms [1][2][3][4][5]. However, learning process from continuous attributes to di ...
... They are always kept in form of clusters. The books that have some kind of similarities among them are placed in the same cluster.. To reduce the complexity shelves are labeled with names. So when a user wants a book of specific kind on specific topic, he or she would only have to go to that particu ...
Data Mining Techniques for Text Mining
... more correct and useful information. In2 in this paper based on new model that is conceptbased which analysis both sentence and documents. The document analysis was based by previous model. This model has two parts: firstly, based on analysis of term and secondly, is based on measure of similarity. ...
... more correct and useful information. In2 in this paper based on new model that is conceptbased which analysis both sentence and documents. The document analysis was based by previous model. This model has two parts: firstly, based on analysis of term and secondly, is based on measure of similarity. ...
Video Mining
... • It will be able to operate without the direct intervention of a user, and be able to control its own focus of attention to some extent • This will in turn influence how it operates in related situations in the future ...
... • It will be able to operate without the direct intervention of a user, and be able to control its own focus of attention to some extent • This will in turn influence how it operates in related situations in the future ...
On a New Scheme on Privacy Preserving Association Rule Mining
... intentionally breach the privacy of the data. On the other hand, illegal data miners would purposely discover the privacy in the data being mined. Illegal data miners come in many forms. In this paper, we focus on a particular sub-class of illegal miners. That is, in our system, illegal data miners ...
... intentionally breach the privacy of the data. On the other hand, illegal data miners would purposely discover the privacy in the data being mined. Illegal data miners come in many forms. In this paper, we focus on a particular sub-class of illegal miners. That is, in our system, illegal data miners ...
Multi Relational Data Mining Approaches: A Data Mining Technique
... ,most of the data are stored in multiple relations .the MRDM aims to discovering interesting knowledge directly from multiple tables without joining the data of multiple tables into a single table explicitly.[11][12][13]. In Multi relational data mining where the relations are available in the form ...
... ,most of the data are stored in multiple relations .the MRDM aims to discovering interesting knowledge directly from multiple tables without joining the data of multiple tables into a single table explicitly.[11][12][13]. In Multi relational data mining where the relations are available in the form ...
Demonstration of clustering rule process on data-set iris.arff
... Roll-up is performed by climbing up a concept hierarchy for the dimension location. Initially the concept hierarchy was "street < city < province < country". On rolling up, the data is aggregated by ascending the location hierarchy from the level of city to the level of country. The data is grouped ...
... Roll-up is performed by climbing up a concept hierarchy for the dimension location. Initially the concept hierarchy was "street < city < province < country". On rolling up, the data is aggregated by ascending the location hierarchy from the level of city to the level of country. The data is grouped ...
A Mixture Model of Clustering Ensembles
... consensus function – O(kNH) for k clusters in the target partition, N patterns, and H clusterings in the ensemble. This results in fast convergence that is comparable to the k-means algorithm. 3. The ability to handle missing data, in this case missing cluster labels (or labels determined to be unkn ...
... consensus function – O(kNH) for k clusters in the target partition, N patterns, and H clusterings in the ensemble. This results in fast convergence that is comparable to the k-means algorithm. 3. The ability to handle missing data, in this case missing cluster labels (or labels determined to be unkn ...
Chapter 7
... Remove outliers (e.g. 10% of points farthest from the regression plane) Minimize median instead of mean of squares (copes with outliers in x and y direction) ...
... Remove outliers (e.g. 10% of points farthest from the regression plane) Minimize median instead of mean of squares (copes with outliers in x and y direction) ...
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.