Có 20+ tài liệu thuộc chủ đề "kiến thức máy tính"
tailieu.vn Xem trực tuyến Tải xuống
MÔN: PHẦN CỨNG MÁY TÍNH. CỨNG MÁY TÍNH. Nhận diện được các đầu kết nối dành cho: Mainboard, HDD, CD/DVD ROM Drive. Phân biệt đầu kết nối cấp nguồn cho CPU: 2 vàng 2 đen, đầu kết nối cho FAN vi xử lý: đỏ - vàng – cam. Cách kết nối trực tiếp. So sánh sự khác biệt cơ bản...
tailieu.vn Xem trực tuyến Tải xuống
Microsoft ASP.NET. Hành trang vào Khóa Học ASP.NET. Nhu liệu (phần mền hay software) tối thiểu phải có để học khoá ASP.NET thành công:. Tại sao ta lại quan tâm và phát triển mạng với ASP.NET. tầm thường của ASP.NET như sau:. Ðây là trang đầu tiên khi dùng Visual Studio.NET:. ASP.NET cho phép ta tự động cập nhật hóa...
tailieu.vn Xem trực tuyến Tải xuống
The workbench includes methods for all the standard Data Mining problems: regression, classification, clustering, association rule mining, and attribute selection. All algorithms and methods take their input in the form of a single relational table, which can be read from a file or generated by a database query.. Exploring the Data. It has six differ- ent panels, accessed by the...
tailieu.vn Xem trực tuyến Tải xuống
Data Mining and Knowledge Discovery Handbook. Data Mining (DM) is the mathematical core of the KDD process, involving the inferring algorithms that explore the data, develop mathematical models and discover significant patterns (implicit or explicit) -which are the essence of useful knowledge. This handbook aims to organize all major concepts, theories, methodologies, trends, challenges and applica- tions of Data Mining...
tailieu.vn Xem trực tuyến Tải xuống
175 11 Data Mining within a Regression Framework. 321 17 Constraint-based Data Mining. 19 A Review of Evolutionary Algorithms for Data Mining. 401 21 Neural Networks For Data Mining. Contents XI 24 Using Fuzzy Logic in Data Mining. 25 Statistical Methods for Data Mining. 523 26 Logics for Data Mining. 541 27 Wavelet Methods in Data Mining. 603 31 Quality...
tailieu.vn Xem trực tuyến Tải xuống
Introduction to Knowledge Discovery and Data Mining. Data Mining (DM) is the core of the KDD process, involving the inferring of algo- rithms that explore the data, develop the model and discover previously unknown patterns. The accessibility and abundance of data today makes Knowledge Discovery and Data Mining a matter of considerable importance and necessity. Given the recent growth of...
tailieu.vn Xem trực tuyến Tải xuống
Full taxonomy – for all the nine steps of the KDD process. We have shown a taxonomy for the DM methods, but a taxonomy is needed for each of the nine steps. Meta-algorithms – algorithms that examine the characteristics of the data in order to determine the best methods, and parameters (including decompositions).. Benefit analysis – to understand the effect...
tailieu.vn Xem trực tuyến Tải xuống
process of data cleansing is also laborious, time consuming, and itself prone to errors.. Useful and powerful tools that automate or greatly assist in the data cleansing process are necessary and may be the only practical and cost effective way to achieve a reasonable quality level in existing data.. Some related research addresses the issues of data quality (Ballou and...
tailieu.vn Xem trực tuyến Tải xuống
M., Piatetsky-Shapiro, G., &. Y., Peng, S., &. Proceedings of The. Y., Li, Z., &. Wang, R., Strong, D., &. S., Peng, S., &. Handling Missing Attribute Values. In this chapter methods of handling missing attribute values in Data Mining are described. In sequential methods, missing attribute values are replaced by known values first, as a preprocessing, then the knowl-...
tailieu.vn Xem trực tuyến Tải xuống
3.2.7 Replacing Missing Attribute Values by the Attribute Mean Restricted to a Concept. A missing attribute value of a numerical attribute is replaced by the arithmetic mean of all known values of the attribute restricted to the concept. For example from Table 3.7, case 3 has missing attribute value for Temperature. Case 3 belong to the concept . The arithmetic...
tailieu.vn Xem trực tuyến Tải xuống
Grzymala-Busse. Grzymala-Busse J.W., Grzymala-Busse W.J., and Goodwin L.K. Proceedings of the Second In- ternational Conference on Rough Sets and Current Trends in Computing RSCTC’2000, Banff, Canada, October . of the Fifth International Workshop on Rough Sets and Soft Computing (RSSC’97) at the Third Joint Conference on Infor- mation Sciences (JCIS’97), Research Triangle Park, NC, March . Grzymala-Busse J.W. Proceedings of...
tailieu.vn Xem trực tuyến Tải xuống
Both this and the constraints can be satisfied by choosing β cq = 0 ∀ q >. Note that this also amounts to a proof that the ’greedy’. approach to PCA dimensional reduction - solve for a single optimal direction (which gives the principal eigenvector as first basis vector), then project your data into the subspace orthogonal to that, then...
tailieu.vn Xem trực tuyến Tải xuống
Notice that the elements are squared distances, despite the name. We can see this as follows. i j = A i j − A R i j − A C i j + A RC i j (4.26) where A C ≡ AQ is the matrix A with each column replaced by the column mean, A R ≡ QA is...
tailieu.vn Xem trực tuyến Tải xuống
The first is the method of principal curves, where the idea is to find that smooth curve that passes through the data in such a way that the sum of short- est distances from each point to the curve is minimized, thus providing a nonlinear, one-dimensional summary of the data (Hastie and Stuetzle, 1989). Theoretical foundations of the poten- tial...
tailieu.vn Xem trực tuyến Tải xuống
M i contains K features out of the remaining features for which the value of Equation 5.2 is smallest. The expected cross entropy between the distribution of the class values, given M i , V i , and the distribution of class values given just M i , is calculated for each feature i. Experiments on natural domains and two...
tailieu.vn Xem trực tuyến Tải xuống
and Maimon, O., Clustering methods, Data Mining and Knowledge Discovery Handbook, pp. and Lavi I., Space Decomposition In Data Mining: A Clustering Ap- proach, Proceedings of the 14th International Symposium On Methodologies For Intel- ligent Systems, Maebashi, Japan, Lecture Notes in Computer Science, Springer-Verlag, 2003, pp. In Proceedings of the Seventh IEEE International Conference on Tools with Artificial In- telligence,...
tailieu.vn Xem trực tuyến Tải xuống
The above mentioned methods are all time-insensitive while dynamic-qualitative discretization (Mora et al., 2000) is typically time-sensitive. The first ap- proach is to use statistical information about the preceding values observed from the time series to select the qualitative value which corresponds to a new quantita- tive value of the series. Two consecutive quantitative values correspond to the same qualitative...
tailieu.vn Xem trực tuyến Tải xuống
x n , and the sample standard deviation, S n . In inward testing, or forward selection methods, at each step of the procedure the. “most extreme observation”, i.e., the one with the largest outlyingness measure, is tested for being an outlier. The statistics are calculated on the basis of the reduced sample and then the removed observations in the...
tailieu.vn Xem trực tuyến Tải xuống
Ruts I., Rousseeuw P., ”Computing Depth Contours of Bivariate Point Clouds,” In Compu- tational Statistics and Data Analysis . of the Seventh ACM-SIGKDD Conference on Knowledge Discovery and Data Mining, SF, CA, 2001.. J., Huang Z., ”Mining the knowledge mine: The hot spots methodology for mining large real world databases,” In Abdul Sattar, editor, Advanced Topics in Artificial Intelligence, volume...