Mobility, Data Mining and Privacy: A Vision of Convergence
... explicitly tailored to the analysis of mobility with reference to geography, at appropriate scales and granularity. In fact, movement always occurs in a given physical space, whose key semantic features are usually represented by geographical maps; as a consequence, the geographical background knowl ...
... explicitly tailored to the analysis of mobility with reference to geography, at appropriate scales and granularity. In fact, movement always occurs in a given physical space, whose key semantic features are usually represented by geographical maps; as a consequence, the geographical background knowl ...
WSARE: What`s Strange About Recent Events
... Our results were obtained by running the simulator for 180 simulated days with the epidemic, named Epidemic0, introduced to the environment on the 90th day. Epidemic0 had a target demographic group of males 50-59 years old. Additionally, there were nine non-epidemic background diseases that spontane ...
... Our results were obtained by running the simulator for 180 simulated days with the epidemic, named Epidemic0, introduced to the environment on the 90th day. Epidemic0 had a target demographic group of males 50-59 years old. Additionally, there were nine non-epidemic background diseases that spontane ...
A Density Based Dynamic Data Clustering Algorithm based on
... relationship among data. Dynamic clustering is a mechanism to adopt and discover clusters in real time environments. There are many applications such as incremental data mining in data warehousing applications, sensor network, which relies on dynamic data clustering algorithms. Approach: In this wor ...
... relationship among data. Dynamic clustering is a mechanism to adopt and discover clusters in real time environments. There are many applications such as incremental data mining in data warehousing applications, sensor network, which relies on dynamic data clustering algorithms. Approach: In this wor ...
I. Introduction
... algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to this functionality. Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection. ...
... algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to this functionality. Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection. ...
A Survey: Outlier Detection in Streaming Data Using
... cluster. The author receives into account the choice of k centers and variable size of buckets with the help of which space can be efficiently use at the time of clustering. Most established algorithms makes very difficult problem in clustering by falling their quality for a better competence. [16], ...
... cluster. The author receives into account the choice of k centers and variable size of buckets with the help of which space can be efficiently use at the time of clustering. Most established algorithms makes very difficult problem in clustering by falling their quality for a better competence. [16], ...
A Classification Technique using Associative
... The data analysis algorithms (or data mining algorithms, as they are more popularly known nowadays) can be divided into three major categories based on the nature of their information extraction [1]: Clustering (also called segmentation or unsupervised learning), Predictive modeling (also called cla ...
... The data analysis algorithms (or data mining algorithms, as they are more popularly known nowadays) can be divided into three major categories based on the nature of their information extraction [1]: Clustering (also called segmentation or unsupervised learning), Predictive modeling (also called cla ...
2. Principles of Data Mining 2.1 Learning from Examples
... • Sets of instances covered by h1 and h2: h2 imposes fewer constraints than h1 and therefore classifies more instances x as positive than h1 • Let hj and hk be hypotheses from H, i.e. boolean-valued functions defined over X. Then hj is more general than or equal to hk (hj ≥ hk) if and only if ∀x ∈ X ...
... • Sets of instances covered by h1 and h2: h2 imposes fewer constraints than h1 and therefore classifies more instances x as positive than h1 • Let hj and hk be hypotheses from H, i.e. boolean-valued functions defined over X. Then hj is more general than or equal to hk (hj ≥ hk) if and only if ∀x ∈ X ...
Classification using Association Rule Mining
... constructs a highly compact data structure (FP-tree) to compress the original transaction database. It focuses on the frequent pattern (fragment) growth and eliminate repeated database scan. The performance study by Han’s group shows that FP-growth is more efficient than Apriori algorithm. ...
... constructs a highly compact data structure (FP-tree) to compress the original transaction database. It focuses on the frequent pattern (fragment) growth and eliminate repeated database scan. The performance study by Han’s group shows that FP-growth is more efficient than Apriori algorithm. ...
Handling missing data in trees: surrogate splits or statistical imputation
... class) to be used as a training set, with di erent percentages of missing data in the covariates. Then we built trees as follows 1. On the incomplete training set, using surrogate splits. 2. On one or more completed data sets using (multiple) imputation. In both cases the trees were built using 10-f ...
... class) to be used as a training set, with di erent percentages of missing data in the covariates. Then we built trees as follows 1. On the incomplete training set, using surrogate splits. 2. On one or more completed data sets using (multiple) imputation. In both cases the trees were built using 10-f ...
A Novel Method for Protecting Sensitive Knowledge in Association
... into Marked-Set directly • 2. for all remainder P in PH do – if (P has no Pair-Subsets included in MarkedSet) • Generate k groups, k = # of all Pair-Subsets of P • Class label of group is named by each PairSubsets of P • P is stored in each group ...
... into Marked-Set directly • 2. for all remainder P in PH do – if (P has no Pair-Subsets included in MarkedSet) • Generate k groups, k = # of all Pair-Subsets of P • Class label of group is named by each PairSubsets of P • P is stored in each group ...
Parameter reduction for density-based clustering
... Clustering on large datasets has become one of the most intensively studied areas with increasing data volumes. One of the problems of clustering on large datasets is minimal domain knowledge to determine the input parameters. In the density based clustering, the main input is the minimum neighborho ...
... Clustering on large datasets has become one of the most intensively studied areas with increasing data volumes. One of the problems of clustering on large datasets is minimal domain knowledge to determine the input parameters. In the density based clustering, the main input is the minimum neighborho ...
earth-observation data access: a knowledge discovery
... used either for classification purposes or in the knowledge discovery framework. The second input is the metadata in form of annotations included in xml files or information in the header of geotiff files. The output is a set of descriptors. Finally, the further steps the EO image (raster data) will ...
... used either for classification purposes or in the knowledge discovery framework. The second input is the metadata in form of annotations included in xml files or information in the header of geotiff files. The output is a set of descriptors. Finally, the further steps the EO image (raster data) will ...
Optimising operational costs using Soft Computing techniques
... better prepared it is to perform to a high degree of accuracy. This may be demonstrated through a comparison with non-interpretable models. One of the difficulties in developing a broad solution to this problem is that the literature only contains different ad hoc solutions to specific problems [18, ...
... better prepared it is to perform to a high degree of accuracy. This may be demonstrated through a comparison with non-interpretable models. One of the difficulties in developing a broad solution to this problem is that the literature only contains different ad hoc solutions to specific problems [18, ...
Appendix: The WEKA Data Mining Software
... Knowledge-flow interface can handle incremental updates. It can load and preprocess individual instances before feeding them into incremental learning algorithms. Knowledge-flow also provides nodes for visualization and evaluation. ...
... Knowledge-flow interface can handle incremental updates. It can load and preprocess individual instances before feeding them into incremental learning algorithms. Knowledge-flow also provides nodes for visualization and evaluation. ...
Document
... The range of data values or data quality in an operational system may exceed the expectations of designers at the time validation and transformation rules are specified. Data profiling of a source during data analysis can identify the data conditions that must be managed by transform rules specifica ...
... The range of data values or data quality in an operational system may exceed the expectations of designers at the time validation and transformation rules are specified. Data profiling of a source during data analysis can identify the data conditions that must be managed by transform rules specifica ...
Towards Linked Open Data enabled Data Mining
... in the input dataset should be represented with as simple feature set as possible that fully describes all target concepts, e.g., Occam’s razor approach. To provide a good generalization such approaches should appropriately address the bias-variance dilemma, i.e., the generated features should be ge ...
... in the input dataset should be represented with as simple feature set as possible that fully describes all target concepts, e.g., Occam’s razor approach. To provide a good generalization such approaches should appropriately address the bias-variance dilemma, i.e., the generated features should be ge ...
IEEE Paper Template in A4 (V1)
... of already known attacks. Anomaly detection (behaviour-based) refers to detecting patterns in a given dataset that do not conform to an established normal behaviour. It also attempts to estimate the abnormal behaviour of the system to be protected and generate anomaly alarm whenever the deviation be ...
... of already known attacks. Anomaly detection (behaviour-based) refers to detecting patterns in a given dataset that do not conform to an established normal behaviour. It also attempts to estimate the abnormal behaviour of the system to be protected and generate anomaly alarm whenever the deviation be ...
View PDF - CiteSeerX
... measures were calculated for normalized values of output variables except for MAPE, where in order to avoid the division by zero, actual and predicted prices had to be denormalized. It can be observed that, some measures, especially those based on square errors reveal similar relationships between m ...
... measures were calculated for normalized values of output variables except for MAPE, where in order to avoid the division by zero, actual and predicted prices had to be denormalized. It can be observed that, some measures, especially those based on square errors reveal similar relationships between m ...
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.