With the development of Location-Based Social Network （LBSN） Point-Of-Interest （POI） recommendation， an effective way to alleviate information overload， has attracted much attention. As user check-in data are implicit feedback data and very sparse， a hybrid POI recommendation model based on geographic preference ranking was proposed to effectively capture the user preference for POIs from check-in data. First， considering the implicit feedback characteristics of check-in data and the spatial constraint of user activities， by calculating the influence of POI distances on POI ranking based on the traditional Bayesian personalized Ranking （BPR） model， a weighted BPR model named GWBPR （Geo-Weighted Bayesian Personalized Ranking） was proposed. Then， aiming at the sparsity of user check-in data， by further integrating Logistic Matrix Factorization （LMF） model with GWBPR model， a hybrid model GWBPR-LMF （GWBPR with LMF） was proposed. Experimental results on two real datasets， Foursquare and Gowalla， show that GWBPR-LMF model outperforms the comparison models like BPR， LMF and SAE-NAD （Self-Attentive Encoder and Neighbor-Aware Decoder）. Compared with the relatively good-performance model SAE-NAD， GWBPR-LMF model improves the precision， recall， F1 score， mean Average Precision （mAP） and Normalized Discounted Cumulative Gain （NDCG） by 44.9%， 57.1%， 78.4%， 55.3%， and 40.0% averagely and respectively on Foursquare dataset， and 3.0%， 6.4%， 4.6%， 11.7%， and 4.2% averagely and respectively on Gowalla dataset.

Dynamic feature selection algorithms can improve the time efficiency of processing dynamic data. Aiming at the problem that there are few unsupervised dynamic feature selection algorithms based on fuzzy-rough sets， an Unsupervised Dynamic Fuzzy-Rough set based Feature Selection （UDFRFS） algorithm was proposed under the condition of features arriving in batches. First， by defining a pseudo triangular norm and new similarity relationship， the process of updating fuzzy relation value was performed on the basis of existing data to reduce unnecessary calculation. Then， by utilizing the existing feature selection results， dependencies were adopted to judge if the original feature part would be recalculated to reduce the redundant process of feature selection， and the feature selection was further speeded up. Experimental results show that compared to the static dependency-based unsupervised fuzzy-rough set feature selection algorithm， UDFRFS can achieve the time efficiency improvement of more than 90 percentage points with good classification accuracy and clustering performance.

In order to reduce the redundant information of hyperspectral image data， optimize the computational efficiency and improve the effectiveness of subsequent applications of image data， a hyperspectral band selection algorithm based on Neighborhood Entropy （NE） was proposed. Firstly， in order to efficiently calculate the neighborhood subset of samples， the Local Sensitive Hashing （LSH） was used as the nearest neighbor search strategy. Then， the NE theory was introduced to measure the Mutual Information （MI） between bands and classes， and minimization of the conditional entropy between feature sets and class variables was used as a method to select effective bands. Finally， two datasets were used to carry out classification experiments through Support Vector Machine （SVM） and Random Forest （RM）. Experimental results show that， compared with four MI based feature selection algorithms， from the perspectives of overall accuracy and Kappa coefficient， the proposed algorithm can select effective band subset within 30 bands faster and achieve local optimization. Some experimental results of the proposed algorithm reach 92.99% and 0.860 8 at the global optimum on overall accuracy and Kappa coefficient respectively， verifying that the proposed algorithm can effectively deal with hyperspectral band selection problem.

The spatial co-location pattern is a subset of spatial features whose instances frequently appear together in the neighborhoods. Co-location pattern mining methods usually assume that spatial instances are independent to each other， adopt a participation rate， which is the frequency of spatial instances participating in pattern instances， to measure the importance of spatial features in the co-location pattern， and adopt a participation index， which is the minimal participation rate of spatial features， to measure the interest of patterns. These methods neglect some important relationships between spatial features. Therefore， the co-location pattern with dominant feature was proposed to reveal the dominant relationship between spatial features. The existing method for mining co-location pattern with dominant feature is based on the traditional co-location pattern mining and its clique instance model. However， the clique instance model may neglect the non-clique dominant relationship between spatial features. Motivated by the above， the dominant feature mining of spatial sub-prevalent co-location patterns was studied based on the star instance model to better reveal the dominant relationship between spatial features and mine more valuable co-location patterns with dominant feature. Firstly， two metrics to measure feature’s dominance were defined. Secondly， an effective algorithm for mining co-location pattern with dominant feature was designed. Finally， the experimental results on both synthetic and real datasets show that the proposed mining algorithm is efficient and the co-location pattern with dominant feature is pratical.