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... is additionally supposed to profit from ongoing trends in hardware development. E.g. [13] suggests that the transfer rates of disk drives continue to improve much faster than the rotational delay time. As a consequence the optimum page size with respect to I/O will even increase. As we will show in ...
... is additionally supposed to profit from ongoing trends in hardware development. E.g. [13] suggests that the transfer rates of disk drives continue to improve much faster than the rotational delay time. As a consequence the optimum page size with respect to I/O will even increase. As we will show in ...
Learning temporal relations in smart home data.
... problem of learning temporal relations between time intervals in smart home data, which includes physical activities (such as taking pills while at home) and instrumental activities (such as turning on lamps and electronic devices). The purpose of this work is to identify interesting temporal patter ...
... problem of learning temporal relations between time intervals in smart home data, which includes physical activities (such as taking pills while at home) and instrumental activities (such as turning on lamps and electronic devices). The purpose of this work is to identify interesting temporal patter ...
On the Necessary and Sufficient Conditions of a Meaningful
... an adaptive proximity function of two data points In fact, the important issue is how to design a meanon individual attributes separately. The proximity ingful distance (or proximity) function for high dimenof two points is the aggregation of each attributive sional data space. The authors in [1] pr ...
... an adaptive proximity function of two data points In fact, the important issue is how to design a meanon individual attributes separately. The proximity ingful distance (or proximity) function for high dimenof two points is the aggregation of each attributive sional data space. The authors in [1] pr ...
An Intergrated Data Mining and Survival Analysis Model for
... (2) Data preparation and Data extraction. Extracting necessary customer behavior attributes from data warehouse. (3) Clustering. Using data mining clustering methods such as K-means to cluster customers based on similar hazard/survival possibility. (4) Appending a new attribute: Cluster Number. Afte ...
... (2) Data preparation and Data extraction. Extracting necessary customer behavior attributes from data warehouse. (3) Clustering. Using data mining clustering methods such as K-means to cluster customers based on similar hazard/survival possibility. (4) Appending a new attribute: Cluster Number. Afte ...
R u t c o r Research Logical Analysis of Multi-Class
... entry stores the differentiability rate of class Ci from class Cj , that is, the ratio of observations in Ci covered by patterns in M which do not cover (or only cover a small proportion of) observations in Cj . The authors of [47] observed that their second approach produces less accurate classific ...
... entry stores the differentiability rate of class Ci from class Cj , that is, the ratio of observations in Ci covered by patterns in M which do not cover (or only cover a small proportion of) observations in Cj . The authors of [47] observed that their second approach produces less accurate classific ...
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)
... brief statistics Diabetes data set on various classification algorithms like SVM, KNN, ID3, CART and C5.0 to classify the diabetes data. They have compared the classification accuracy of these models. SVM gives best classification accuracy as 81.77% compare to others. 2.3 Classwise K Nearest Neighbo ...
... brief statistics Diabetes data set on various classification algorithms like SVM, KNN, ID3, CART and C5.0 to classify the diabetes data. They have compared the classification accuracy of these models. SVM gives best classification accuracy as 81.77% compare to others. 2.3 Classwise K Nearest Neighbo ...
Generalized k-means based clustering for temporal data under
... an honor to be her Ph.D. student. It is not often that one finds an adviser and colleague that always finds the time for listening to the little problems and roadblocks that unavoidably crop up in the course of performing research. Her technical and editorial advice was essential to the completion o ...
... an honor to be her Ph.D. student. It is not often that one finds an adviser and colleague that always finds the time for listening to the little problems and roadblocks that unavoidably crop up in the course of performing research. Her technical and editorial advice was essential to the completion o ...
Data Mining - PhD in Information Engineering
... (produces exactly the same predictions) If x ≤ 1.2 then class = b If x > 1.2 and y ≤ 2.6 then class = b ...
... (produces exactly the same predictions) If x ≤ 1.2 then class = b If x > 1.2 and y ≤ 2.6 then class = b ...
A Survey on Pre-processing and Post-processing
... represents missing value for xp attribute. An instance may contain several missing values. Machine Learning (ML) approaches are not designed to deal with missing values and also produce incorrect results if implemented with this drawback. Before applying machine learning approach, it is essential ei ...
... represents missing value for xp attribute. An instance may contain several missing values. Machine Learning (ML) approaches are not designed to deal with missing values and also produce incorrect results if implemented with this drawback. Before applying machine learning approach, it is essential ei ...
Mining Frequent Spatio-Temporal Patterns from
... performed including frequency of events for natural periods like weeks and hours. Also the percentage of users according to the number of events generated and the number of days that have events during the period of data collection was analyzed. The figure 1 shows different information of the events ...
... performed including frequency of events for natural periods like weeks and hours. Also the percentage of users according to the number of events generated and the number of days that have events during the period of data collection was analyzed. The figure 1 shows different information of the events ...
MapReduce-Based Pattern Finding Algorithm
... framework to speed up pattern finding and avoid running-out-of memory in a PC-cluster environment. We design a MapReduce-based Pattern Finding algorithm (MRPF) that provides good efficiency and scalability. We also apply it in prescription network and successfully find some commonly used prescriptio ...
... framework to speed up pattern finding and avoid running-out-of memory in a PC-cluster environment. We design a MapReduce-based Pattern Finding algorithm (MRPF) that provides good efficiency and scalability. We also apply it in prescription network and successfully find some commonly used prescriptio ...
