Document
... vector models, including imagery and georeferenced multimedia, as well as dynamic data types (video streams, animation). ...
... vector models, including imagery and georeferenced multimedia, as well as dynamic data types (video streams, animation). ...
Fuzzy Association Rules
... data are easily available and mining can thereby be done quickly. Discovered rules can help online-shops in personalizing their website and cross-selling their products by making recommendations. New transactions can be used quickly for mining and give the company a competitive advantage. A problem ...
... data are easily available and mining can thereby be done quickly. Discovered rules can help online-shops in personalizing their website and cross-selling their products by making recommendations. New transactions can be used quickly for mining and give the company a competitive advantage. A problem ...
Anomaly detection: A survey - The Distributed Systems Group
... important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with. Cate ...
... important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with. Cate ...
Contents
... Databases are rich with hidden information that can be used for intelligent decision making. Classification is a form of data analysis that extracts models describing important data classes. Such models, called classifiers, predict categorical (discrete, unordered) class labels. For example, we can ...
... Databases are rich with hidden information that can be used for intelligent decision making. Classification is a form of data analysis that extracts models describing important data classes. Such models, called classifiers, predict categorical (discrete, unordered) class labels. For example, we can ...
Overview and Detail - Georgia Institute of Technology
... • Is each stock a data case, or is a price on a particular day a case, with the stock name as one of the other variables? • Confusion between data entity and data cases ...
... • Is each stock a data case, or is a price on a particular day a case, with the stock name as one of the other variables? • Confusion between data entity and data cases ...
Frequent pattern mining: current status and future directions
... and/or multidimensional space, it is natural to extend mining frequent itemsets and their corresponding association rules to multi-level and multidimensional space. Multilevel association rules involve concepts at different levels of abstraction, whereas multidimensional association rules involve mo ...
... and/or multidimensional space, it is natural to extend mining frequent itemsets and their corresponding association rules to multi-level and multidimensional space. Multilevel association rules involve concepts at different levels of abstraction, whereas multidimensional association rules involve mo ...
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... Table 7.2 – Genetic Algorithm Parameters for Recalibrated Stickout and Release Time Series...................................................................................................................121 Table 7.3 – Recalibrated Stickout and Release Results (Observed) .......................... ...
... Table 7.2 – Genetic Algorithm Parameters for Recalibrated Stickout and Release Time Series...................................................................................................................121 Table 7.3 – Recalibrated Stickout and Release Results (Observed) .......................... ...
Document Clustering: A Detailed Review
... high computational overhead, making it difficult for realworld text data. In feature selection, subsets of features are selected directly. These algorithms are widely used in real-world tasks due to their efficiency, but are based on greedy strategies rather than optimal solutions. So, a unified opt ...
... high computational overhead, making it difficult for realworld text data. In feature selection, subsets of features are selected directly. These algorithms are widely used in real-world tasks due to their efficiency, but are based on greedy strategies rather than optimal solutions. So, a unified opt ...
Doria.fi Bitstream Handle Holmbom Annika
... The amount of data stored in company data bases has increased exponentially over the years and become difficult to handle. This research area is the subject of much current interest, not only in academia but also in practice, as is shown by several magazines and blogs that are covering topics on how ...
... The amount of data stored in company data bases has increased exponentially over the years and become difficult to handle. This research area is the subject of much current interest, not only in academia but also in practice, as is shown by several magazines and blogs that are covering topics on how ...
Data Warehousing and Mining
... PREFACE The objective of data mining is to extract the relevant information from a large collection of information. The large of amount of data exists due to advances in sensors, information technology, and high-performance computing which is available in many scientific disciplines. These data set ...
... PREFACE The objective of data mining is to extract the relevant information from a large collection of information. The large of amount of data exists due to advances in sensors, information technology, and high-performance computing which is available in many scientific disciplines. These data set ...
supervised descriptive rule induction
... 1991), which is defined as “the non-trivial extraction of implicit, unknown, and potentially useful information from data” (Frawley et al., 1991). The concept knowledge discovery from databases (KDD) emerged in 1989 to refer to the process of finding interesting patterns and models in data. Accordin ...
... 1991), which is defined as “the non-trivial extraction of implicit, unknown, and potentially useful information from data” (Frawley et al., 1991). The concept knowledge discovery from databases (KDD) emerged in 1989 to refer to the process of finding interesting patterns and models in data. Accordin ...
Mining periodic behaviors of object movements for animal and
... (Yang et al. 2000), surprising periodic patterns (Yang et al. 2004), patterns with gap penalties (Yang et al. 2002), and higher level patterns (Wang et al. 2001). Asynchronous patterns (Yang et al. 2000) are the periodic patterns that may present only within a subsequence and whose occurrences may b ...
... (Yang et al. 2000), surprising periodic patterns (Yang et al. 2004), patterns with gap penalties (Yang et al. 2002), and higher level patterns (Wang et al. 2001). Asynchronous patterns (Yang et al. 2000) are the periodic patterns that may present only within a subsequence and whose occurrences may b ...
Visual Quality Assessment of Subspace Clusterings
... the algorithm’s results against original assumptions. In this paper, we tackle the problem of visually evaluating the quality of one subspace clustering result. We present a novel open-source evaluation framework, called SubEval. It enhances standard evaluation approaches with effective visualizatio ...
... the algorithm’s results against original assumptions. In this paper, we tackle the problem of visually evaluating the quality of one subspace clustering result. We present a novel open-source evaluation framework, called SubEval. It enhances standard evaluation approaches with effective visualizatio ...
Big Data Technology - Hadoop, MapReduce, and Spark
... • Hive: data warehousing application based on Hadoop. – Query language is HiveQL, which looks similar to ...
... • Hive: data warehousing application based on Hadoop. – Query language is HiveQL, which looks similar to ...
PI Hofgesang 08-10-2009
... learned its values in sharing creative ideas, in supporting cooperation, and to strain after mutually advantageous situations for all parties involved in a business process. The relaxed and professional atmosphere and friendly colleagues highly facilitated my work. The marketing research group, wher ...
... learned its values in sharing creative ideas, in supporting cooperation, and to strain after mutually advantageous situations for all parties involved in a business process. The relaxed and professional atmosphere and friendly colleagues highly facilitated my work. The marketing research group, wher ...
Data Warehousing (DW) Online Analytical Processing (OLAP) Data
... Ö data is selected and organized so support business analysis Ö Optimized for query and analysis Ö Objects (facts) and their determining factors (dimensions) are linked together Ö Not to support OLTP ...
... Ö data is selected and organized so support business analysis Ö Optimized for query and analysis Ö Objects (facts) and their determining factors (dimensions) are linked together Ö Not to support OLTP ...
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.