Form1.cs是應用聚類算法DBSCAN (density-Based Spatical Clustering of Application with Noise)的示例，可以通過兩個參數EPS和MinPts調節聚類。DBSCAN.cs是全部算法的實現文件，聚類算法的進一步信息請參考“數據挖掘”或者相關書籍。聚類示例數據來自于sxdb.mdb，一個Access數據庫。
Form1.cs是應用聚類算法DBSCAN (density-Based Spatical Clustering of Application with Noise)的示例，可以通過兩個參數EPS和MinPts調節聚類。
DBSCAN.cs是實現文件，聚類算法的進一步信息請參考“數據挖掘”或者相關書籍
聚類示例數據來自于sxdb.mdb，一個Access數據庫
DBSCAN是一個基于密度的聚類算法。改算法將具有足夠高度的區域劃分為簇，并可以在帶有“噪聲”的空間數據庫中發現任意形狀的聚類。-DBSCAN is a density-Based clustering algorithm. Algorithm change will have enough height to the regional cluster. and to be with the "noise" of the spatial database found clusters of arbitrary shape.
This LDPC software is intended as an introduction to LDPC codes computer based simulation. The pseudo-random irregular low density parity check matrix is based on Radford M. Neal’s programs collection, which can be found in [1]. While Neal’s collection is well documented, in my opinion, C source codes are still overwhelming, especially if you are not knowledgeable in C language. My software is written for MATLAB, which is more readable than C. You may also want to refer to another MATLAB based LDPC source codes in [2], which has different flavor of code-writing style (in fact Arun has error in his log-likelihood decoder).
This LDPC software is intended as an introduction to LDPC codes computer based simulation. The pseudo-random irregular low density parity check matrix is based on Radford M. Neal’s programs collection, which can be found in [1]. While Neal’s collection is well documented, in my opinion, C source codes are still overwhelming, especially if you are not knowledgeable in C language. My software is written for MATLAB, which is more readable than C. You may also want to refer to another MATLAB based LDPC source codes in [2], which has different flavor of code-writing style (in fact Arun has error in his log-likelihood decoder).
In this paper we present a classifier called bi-density twin support vector machines (BDTWSVMs) for data classification. In the training stage, BDTWSVMs first compute the relative density degrees for all training points using the intra-class graph whose weights are determined by a local scaling heuristic strategy, then optimize a pair of nonparallel hyperplanes through two smaller sized support vector machine (SVM)-typed problems. In the prediction stage, BDTWSVMs assign to the class label depending
on the kernel density degree-based distances from each test point to the two hyperplanes. BDTWSVMs not only inherit good properties from twin support vector machines (TWSVMs) but also give good description for data points. The experimental results on toy as well as publicly available datasets
indicate that BDTWSVMs compare favorably with classical SVMs and TWSVMs in terms of generalization
