The density clustering algorithm has been widely used because of its robustness to noise and the ability to find clusters of any shapes. However， in practical applications， this type of algorithms faces the problem of poor clustering effect due to the uneven distribution of the densities of different clusters in the dataset and the difficulty of distinguishing the borders between clusters. In order to solve the above problem， a Varied Density Clustering algorithm based on Border point Detection （VDCBD） was proposed. Firstly， the border points between varied density clusters were recognized based on the given relative density measurement method to enhance the separability of adjacent clusters. Secondly， the points in the non-border area were clustered to find the core class structures of the dataset. Secondly， the detected border points were allocated to the corresponding core class structures according to the principle of high-density neighbor allocation. Finally， the noise points in the dataset were recognized based on the class structure information. The proposed algorithm was compared and analyzed with the clustering algorithms such as K-means， Density-Based Spatial Clustering of Applications with Noise （DBSCAN）algorithm， Density Peaks Clustering Algorithm （DPCA）， CLUstering based on Backbone （CLUB）algorithm， Border Peeling clustering （BP）algorithm on artificial datasets and UCI datasets. Experimental results show that the proposed algorithm can effectively solve the problems of uneven distribution of density and indistinguishable borders， and is superior to the existing algorithms on the evaluation indicators of Adjusted Rand Index （ARI）， Normalized Mutual Information （NMI）， F-Measure （FM）， and Accuracy （ACC）； in the analysis of operating efficiency， when the data size is relatively large， the operating efficiency of VDCBD is higher than those of DPCA， CLUB and BP algorithms.

Aiming at the problem that the complexity of cervical cell image fine segmentation makes it difficult to achieve automatic abnormal cell screening based on cell image segmentation, a cervical cell classification algorithm without fine segmentation step was proposed. Firstly, a new feature named MAXimum Section (MAXSection) was defined for describing the distribution of pixel values, and was combined with Back Propagation (BP) neural network and Selective Search algorithm to realize the accurate extraction of nucleus Region Of Interest (ROI) (the highest accuracy was 100%). Secondly, two parameters named estimated length and estimated width were defined based on MAXSection to describe morphological changes of abnormal nucleus. Finally, according to the characteristic of absolute enlargement of cervical nucleus when cervical cancer occurs, the classification of abnormal nucleus (at least one parameter of estimated length and width is greater than 65) and normal nucleus (estimated length and width are both less than 65) can be realized by using the above two parameters. Experimental results show that the proposed algorithm has screening accuracy of 98.89%, sensitivity of 98.18%, and specificity of 99.20%. The proposed algorithm can complete the total process from the input of whole Pap smear image to the output of final screening results, realizing the automation of abnormal cervical cell screening.

Based on neighborhood-based rough set model and covering-based rough set model, two matroidal structures which were matroid induced by neighborhood upper approximation number and matroid induced by covering upper approximation number were constructed. On one hand, two types of upper approximation number were defined through generalized rough set, and they were proven to satisfy rank function axiom in matroid theory, thus two types of matroids were obtained from the viewpoint of the rank function. On the other hand, some properties, such as independent sets, circuits, closures, closed sets, were proposed through rough set approach. Moreover, the concentions between upper approximation operators and closure operators were investigated. Futhuremore, the relationship between the covering and the matroid was studied. Result shows that elements and any union of them in covering are the closed sets of matroid induced by covering upper approximation number.

In view of the real-time, sparse and massive characteristics of micro-blog, a topic discovery model based on real-time co-occurrence network was proposed. Firstly, the set of keywords was extracted from the primitive data by the model, and the relationship weights was calculated on the basis of the time parameter to structure the word co-occurrence network. Then, sparsity could be reduced by finding potential features of a strong correlation based on weight adjustment coefficient. Secondly, the topic incremental clustering could be achieved by using the improved Single-Pass algorithm. Finally, the feature words of each topic were sorted by heat calculation, so the most representative keywords of the topic were got. The experimental results show that the accuracy and comprehensive index of the proposed model increase 6%, 8% respectively compared with the Single-Pass algorithm. The experimental results prove the validity and accuracy of the proposed model.

Focusing on the issue that current fully homomorphic encryption schemes are not practical, Gentry-Sahai-Waters (GSW) homomorphic encryption scheme was improved and a leveled fully homomorphic encryption scheme based on Ring Learning with Error (Ring-LWE) and GSW was proposed. Firstly, a basic public key encryption scheme was constructed on Ring-LWE problem, the approximate eigenvector method was used to make it have homomorphic addition and multiplication properties, and the randomized function technique was introduced to simplify the analysis of noise blow-up. Secondly, the correctness and security of the proposed scheme was proved, the correctness of homomorphic addition, multiplication and NAND operation was analyzed in detail. Finally, security parameter was set in accordance with the noise blow-up with homomorphic evaluation and the security of Ring-LWE problem, fast Fourier transformation was adopted to reduce the computational complexity of polynomial multiplication, then a leveled fully homomorphic encryption scheme was given. The size of the pubic key in new scheme is shorter than that in GSW and the computational complexity of NAND gate is reduced from Õ(( nL) ^2.37) to Õ( nL ²).

To deal with the combinatorial optimization problem in the era of big data, a parallel Ant Colony Optimization (ACO) algorithm based on Spark, a framework for the distributed memory computing, was presented. To achieve the parallelization of the phase of solution construction in ant colony optimization, a class of ants was encapsulated to a resilient distributed dataset and the corresponding transformation operators were given. The simulation results in solving the Traveling Salesman Problem (TSP) prove the feasibility of the proposed parallel algorithm. Under the same experimental environment, the comparison results between MapReduce based ant colony algorithm and the proposed algorithm show that the proposed algorithm significantly improves the optimization speed at least ten times than the MapReduce one.

Rate control algorithm(JVT-H017) has been given in encoder reference software JM(Joint Model) 8.4.However,in JM a linear model is used to predict the MAD,which costs too much operations and exists deviations.A new frame-layer non-linear quantization parameter calculation model was proposed.Simulation results show that,comparing with JVT-H017,the H.264 coder using the proposed algorithm can control the bit rate more accurately and obtain a higher PSNR and visual quality.Especially when the two schemes are applied to the sequence with scene changes,no buffer overflow or underflow is guaranteed.