Data analysis and navigation in high
... charts unexplored chemical space. The compounds encountered along the way may provide valuable starting points for virtual screening. The use of neural networks based on self-organizing maps is proposed in (Matero et al. 2006). By using a tree-structured self-organizing map it is possible to constru ...
... charts unexplored chemical space. The compounds encountered along the way may provide valuable starting points for virtual screening. The use of neural networks based on self-organizing maps is proposed in (Matero et al. 2006). By using a tree-structured self-organizing map it is possible to constru ...
Data Mining in a Geospatial Decision Support system
... mining techniques shown in Figure 3. (See Harms 2001 for the detailed system.) Our overall goal is to find relationships with droughts and other climatic episodes and with agricultural outcomes, such as crop yield. The proposed techniques are intended as exploratory methods. Thus, iterative and inte ...
... mining techniques shown in Figure 3. (See Harms 2001 for the detailed system.) Our overall goal is to find relationships with droughts and other climatic episodes and with agricultural outcomes, such as crop yield. The proposed techniques are intended as exploratory methods. Thus, iterative and inte ...
A Clustering Methodology of Web Log Data for Learning
... It uses outlier detection and removes extreme values. This step is not performed by the e-learning platform and thus can be embedded into a variety of LMS systems. The produced log file, from the previous step, is filtered, so it includes only the following fields: (i) courseID, which is the identif ...
... It uses outlier detection and removes extreme values. This step is not performed by the e-learning platform and thus can be embedded into a variety of LMS systems. The produced log file, from the previous step, is filtered, so it includes only the following fields: (i) courseID, which is the identif ...
03_PKDD_PHDCluster - NDSU Computer Science
... There are two major approaches for density-based methods. The first approach is represented by DENCLUE [3]. It exploits a density function, e.g., step function or Gaussian function to measure the density in attribute metric space. Clusters are identified by determining density attractors. Thus, clus ...
... There are two major approaches for density-based methods. The first approach is represented by DENCLUE [3]. It exploits a density function, e.g., step function or Gaussian function to measure the density in attribute metric space. Clusters are identified by determining density attractors. Thus, clus ...
Mining Semantic Data for Solving First-rater and Cold
... Use of taxonomies to classify products Induction of abstracts patterns which relate user profiles with categories of products Cold-Start problem: Recommendations based on user profiles Mining Semantic Data for Solving First-rater and Cold-start Problems in Recommender Systems María N. Moreno ...
... Use of taxonomies to classify products Induction of abstracts patterns which relate user profiles with categories of products Cold-Start problem: Recommendations based on user profiles Mining Semantic Data for Solving First-rater and Cold-start Problems in Recommender Systems María N. Moreno ...
KNN Classification and Regression using SAS
... kNN algorithm is expensive in both storage and computation. Therefore, it is important to the factors that contribute to complexity in time and space of this algorithm, thus optimize the implementation in SAS. Optimize Storage kNN classification requires a lot of storage because this is a in-memory ...
... kNN algorithm is expensive in both storage and computation. Therefore, it is important to the factors that contribute to complexity in time and space of this algorithm, thus optimize the implementation in SAS. Optimize Storage kNN classification requires a lot of storage because this is a in-memory ...
Mining BIM Models: Data Representation and Clustering from
... representation. Those algorithms usually expect data in the form of tables in a database, where each line is characterized by a fixed number of columns, with a defined type in each column. If that was the case, the use of BIM models for data mining would be trivial. However, BIM models, as many othe ...
... representation. Those algorithms usually expect data in the form of tables in a database, where each line is characterized by a fixed number of columns, with a defined type in each column. If that was the case, the use of BIM models for data mining would be trivial. However, BIM models, as many othe ...
ICDM-workshop-keynot.. - The University of Texas at Dallas
... I may want my location to be private, but does it make sense if a camera can capture a picture of me? - If there are sensors all over the place, is it meaningful to have privacy preserving surveillance? This suggests that we need application specific privacy It is not meaningful to examine PPDM ...
... I may want my location to be private, but does it make sense if a camera can capture a picture of me? - If there are sensors all over the place, is it meaningful to have privacy preserving surveillance? This suggests that we need application specific privacy It is not meaningful to examine PPDM ...
Improving Quality of Educational Processes Providing
... 1995 and 2005, where they present a review of dierent types of educational systems and how data mining can be applied to each of them. In addition, they describe the data mining techniques that have been applied to educational systems grouping them by task. Al-Radaideh et al. (2006) applied decisio ...
... 1995 and 2005, where they present a review of dierent types of educational systems and how data mining can be applied to each of them. In addition, they describe the data mining techniques that have been applied to educational systems grouping them by task. Al-Radaideh et al. (2006) applied decisio ...
Chapter8 DATA MINING PRIMITIVES, LANGUAGES, AND SYSTEM
... A popular misconception about mining is to expect that data mining can autonomously dig out of the valuable knowledge that is embedded in a given large database, without human intervention or guidance. Although it may at first sound appealing to have an autonomous data mining system, in practice suc ...
... A popular misconception about mining is to expect that data mining can autonomously dig out of the valuable knowledge that is embedded in a given large database, without human intervention or guidance. Although it may at first sound appealing to have an autonomous data mining system, in practice suc ...
An Evaluation of Gene Selection Methods for Multi
... A microarray can contain up to 20,000 features, each of which recognizes mRNA from a single gene, and a relatively small number of experiments or samples (in the order of hundreds or less). As a consequence, the identification of discriminant genes to classifying tissue types, e.g., presence of canc ...
... A microarray can contain up to 20,000 features, each of which recognizes mRNA from a single gene, and a relatively small number of experiments or samples (in the order of hundreds or less). As a consequence, the identification of discriminant genes to classifying tissue types, e.g., presence of canc ...
PDF - JMLR Workshop and Conference Proceedings
... • How many potentially interesting patterns are found (without significance testing)? If there are many patterns, additional significance filtering can help to focus on the most relevant ones. However, also larger correction factors have to be applied in this case. • How much manpower is available f ...
... • How many potentially interesting patterns are found (without significance testing)? If there are many patterns, additional significance filtering can help to focus on the most relevant ones. However, also larger correction factors have to be applied in this case. • How much manpower is available f ...
Fast Distance Metric Based Data Mining Techniques Using P
... objects in the same group are similar and two objects in different groups are dissimilar. Clustering can also be viewed as the process of finding equivalence classes of the data objects where each cluster is an equivalence class. Distance metrics play an important role in data mining. Distance metri ...
... objects in the same group are similar and two objects in different groups are dissimilar. Clustering can also be viewed as the process of finding equivalence classes of the data objects where each cluster is an equivalence class. Distance metrics play an important role in data mining. Distance metri ...
Journal of Theoretical and Applied Information Technology
... Localization and Reconstruction of homogenous objects from electromagnetic scattered fields have been shown to be of great importance, because of their various applications in many areas such as medicine, biology, geophysics and other sciences. In this paper, we propose a c ...
... Localization and Reconstruction of homogenous objects from electromagnetic scattered fields have been shown to be of great importance, because of their various applications in many areas such as medicine, biology, geophysics and other sciences. In this paper, we propose a c ...
A Survey on Clustering Algorithm for Microarray Gene Expression
... at some level, we can obtain a specified number of clusters. By reordering the objects such that the branches of the corresponding dendrogram do not cross, the data set can be arranged with similar objects placed together. There two methods for hierarchical clustering. Agglomerative: start with ever ...
... at some level, we can obtain a specified number of clusters. By reordering the objects such that the branches of the corresponding dendrogram do not cross, the data set can be arranged with similar objects placed together. There two methods for hierarchical clustering. Agglomerative: start with ever ...
k-Attractors: A Partitional Clustering Algorithm for umeric Data Analysis
... The work of Jing et al. (Jing, Ng and Zhexue, 2007) provides a new clustering algorithm called EWKM which is a k-means type subspace clustering algorithm for high-dimensional sparse data. Patrikainen and Meila present a framework for comparing subspace clusterings (Patrikainen and Meila 2006), while ...
... The work of Jing et al. (Jing, Ng and Zhexue, 2007) provides a new clustering algorithm called EWKM which is a k-means type subspace clustering algorithm for high-dimensional sparse data. Patrikainen and Meila present a framework for comparing subspace clusterings (Patrikainen and Meila 2006), while ...
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.