LNAI 1704 - Taming Large Rule Models in Rough Set Approaches
... of nding all models (i.e. reducts) is NP-hard [23], even if it may, at times, be alleviated through the use of appropriate heuristics. In the second approach, the cost of computing is much smaller, for the price of, possibly, not nding the best model. However, the models that are found may be suÆc ...
... of nding all models (i.e. reducts) is NP-hard [23], even if it may, at times, be alleviated through the use of appropriate heuristics. In the second approach, the cost of computing is much smaller, for the price of, possibly, not nding the best model. However, the models that are found may be suÆc ...
Data Mining
... Why data post-processing? (2) • A post-processing methodology is useful, if o the desired focus is not known in advance (the search process cannot be optimized to look only for the interesting patterns) o there is an algorithm that can produce all patterns from a class of potentially interesting pa ...
... Why data post-processing? (2) • A post-processing methodology is useful, if o the desired focus is not known in advance (the search process cannot be optimized to look only for the interesting patterns) o there is an algorithm that can produce all patterns from a class of potentially interesting pa ...
Tutorial on Spatial and Spatio-Temporal Data Mining (ICDM – 2010)
... Cao, H., Mamoulis, N., and Cheung, D. W. (2005). Mining frequent spatio-temporal sequential patterns. In ICDM ’05: Proceedings of the Fifth IEEE International Conference on Data Mining, pages 82–89, Washington, DC, USA. IEEE Computer Society. Jae-Gil Lee, Jiawei Han, Xiaolei Li, and Hector Gonzalez, ...
... Cao, H., Mamoulis, N., and Cheung, D. W. (2005). Mining frequent spatio-temporal sequential patterns. In ICDM ’05: Proceedings of the Fifth IEEE International Conference on Data Mining, pages 82–89, Washington, DC, USA. IEEE Computer Society. Jae-Gil Lee, Jiawei Han, Xiaolei Li, and Hector Gonzalez, ...
Parallel Fuzzy c-Means Cluster Analysis
... The aim of the FCM cluster analysis algorithm is to determine the best partition for the data being analyzed, by investigating different partitions, represented by the partitions’ centers. Hence, the cluster analysis must integrate the FCM algorithm and the PBM procedure as described above. The clus ...
... The aim of the FCM cluster analysis algorithm is to determine the best partition for the data being analyzed, by investigating different partitions, represented by the partitions’ centers. Hence, the cluster analysis must integrate the FCM algorithm and the PBM procedure as described above. The clus ...
Data Mining:
... From Tables and Spreadsheets to Data Cubes A data warehouse is based on a multidimensional data model which views data in the form of a data cube. A data cube, such as sales, allows data to be modeled and viewed in multiple dimensions. Dimension tables, such as ...
... From Tables and Spreadsheets to Data Cubes A data warehouse is based on a multidimensional data model which views data in the form of a data cube. A data cube, such as sales, allows data to be modeled and viewed in multiple dimensions. Dimension tables, such as ...
System: EpiCS - Operations, Information and Decisions Department
... argue for a different approach to developing predictive models. The domain of knowledge discovery in databases (KDD) is rich with such approaches. One in particular, decision tree induction, embodied in the software C4.52, has been used extensively and successfully to discover prediction models in d ...
... argue for a different approach to developing predictive models. The domain of knowledge discovery in databases (KDD) is rich with such approaches. One in particular, decision tree induction, embodied in the software C4.52, has been used extensively and successfully to discover prediction models in d ...
Visual Data Mining of Web Navigational Data
... representation of compressed data, but also provide insight into the machine learning methods that we use to extract structure from that data. Our visualization system, called WebViz, combines a number of visualization and visual manipulation techniques from a broad range of existing web mining and ...
... representation of compressed data, but also provide insight into the machine learning methods that we use to extract structure from that data. Our visualization system, called WebViz, combines a number of visualization and visual manipulation techniques from a broad range of existing web mining and ...
Time-focused density-based clustering of trajectories of
... researchers. Most of the actual work is focused on two kinds of spatio-temporal data: moving objects trajectories (the topic of this paper), such as traffic data, and geographically referenced events, such as epidemiological and geophysical data collected along several years. Trajectory clustering. ...
... researchers. Most of the actual work is focused on two kinds of spatio-temporal data: moving objects trajectories (the topic of this paper), such as traffic data, and geographically referenced events, such as epidemiological and geophysical data collected along several years. Trajectory clustering. ...
Lecture Notes - Computer Science Department
... will result on a set of attributes that describe what interest us from the domain and what we want to use for the discovery process. According to this representation we can categorize the different kinds of datasets in two groups. The first kind are those that can be represented by a flat structure, ...
... will result on a set of attributes that describe what interest us from the domain and what we want to use for the discovery process. According to this representation we can categorize the different kinds of datasets in two groups. The first kind are those that can be represented by a flat structure, ...
Cluster Center Initialization for Categorical Data Using Multiple
... the first K distinct data objects are chosen as initial Kmodes, whereas the second method calculates the frequencies of all categories for all attributes and assign the most frequent categories equally to the initial Kmodes. The first method may only work if the top K data objects come from disjoint ...
... the first K distinct data objects are chosen as initial Kmodes, whereas the second method calculates the frequencies of all categories for all attributes and assign the most frequent categories equally to the initial Kmodes. The first method may only work if the top K data objects come from disjoint ...
World-Wide Web WWW
... Strong first-order assumption Simple way to capture sequential dependence If each page is a state and if W pages, O(W2), W can be of the order 105 to 106 for a CS dept. of a university To alleviate, we can cluster W pages into M clusters, each assigned a state in the Markov model Clustering can ...
... Strong first-order assumption Simple way to capture sequential dependence If each page is a state and if W pages, O(W2), W can be of the order 105 to 106 for a CS dept. of a university To alleviate, we can cluster W pages into M clusters, each assigned a state in the Markov model Clustering can ...
ch20
... statistical rules and patterns from large databases. A data warehouse archives information gathered from multiple sources, and stores it under a unified schema, at a single site. ...
... statistical rules and patterns from large databases. A data warehouse archives information gathered from multiple sources, and stores it under a unified schema, at a single site. ...
Chapter 20: Data Warehousing and Mining
... statistical rules and patterns from large databases. A data warehouse archives information gathered from multiple sources, and stores it under a unified schema, at a single site. Important for large businesses that generate data from multiple divisions, possibly at multiple sites Data may also ...
... statistical rules and patterns from large databases. A data warehouse archives information gathered from multiple sources, and stores it under a unified schema, at a single site. Important for large businesses that generate data from multiple divisions, possibly at multiple sites Data may also ...
KDD Process
... Why data post-processing? (2) • A post-processing methodology is useful, if o the desired focus is not known in advance (the search process cannot be optimized to look only for the interesting patterns) o there is an algorithm that can produce all patterns from a class of potentially interesting pat ...
... Why data post-processing? (2) • A post-processing methodology is useful, if o the desired focus is not known in advance (the search process cannot be optimized to look only for the interesting patterns) o there is an algorithm that can produce all patterns from a class of potentially interesting pat ...
survey on big data mining platforms, algorithms and
... velocity capture, discovery, and analysis[5]. O’Reilly [6] defines big data is the data that exceeds the processing capacity of conventional database systems. He also explains that the data is very big, moves very fast, or doesn’t fit into traditional database architectures. Further he has extended ...
... velocity capture, discovery, and analysis[5]. O’Reilly [6] defines big data is the data that exceeds the processing capacity of conventional database systems. He also explains that the data is very big, moves very fast, or doesn’t fit into traditional database architectures. Further he has extended ...
Learning temporal relations in smart home data.
... Temporal rule mining and pattern discovery applied to time series data has attracted considerable interest over the last few years. In this paper we consider the problem of learning temporal relations between time intervals in smart home data, which includes physical activities (such as taking pills ...
... Temporal rule mining and pattern discovery applied to time series data has attracted considerable interest over the last few years. In this paper we consider the problem of learning temporal relations between time intervals in smart home data, which includes physical activities (such as taking pills ...
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.