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Landscape design involves many elements, such as plants, water, terrain, roads, and buildings. Each element has its specific design requirements and aesthetic standards. The combination and layout of these elements need to consider multiple factors such as spatial relations, functional requirements, ecological balance, and cultural background, which makes the design process very complex. In order to quantify the landscape layout and scientifically layout the landscape architecture, a parametric design method of landscape architecture layout based on a clustering algorithm was proposed. First, the composition of the landscape is analyzed based on the traditional research results, and the required computer software foundation is discussed. Then, in the corresponding software environment, considering that the impact factors of landscape layout elements include numerical and nonnumerical types, $K$-prototype clustering algorithm is used for processing. Specifically, the Euclidean distance of the $K$-means clustering model is used to measure the numerical impact factors, while the Hamming distance of the $K$ model is used to measure the nonnumerical impact factors, so as to achieve effective clustering of mixed impact factors and determine the layout parameters accordingly. At the same time, the rule matrix method is used to establish the design rules. Finally, based on the above research results, the parametric layout of landscape architecture is realized through a series of processes such as design research, planning and layout, construction and form, construction and management. The experimental results demonstrate that this method effectively extracts layout parameters, of landscape architecture and obtains more clear and logical design rules, thus improving the final design effect.

The students with mental health problems often have no spirit to do anything, and serious students will also suffer from depression or split personality. Therefore, we must take corresponding measures to find students with problems in time. Artificial intelligence has a great impact on ideological and political courses, and how to make intelligent algorithms play an active role in the course will be an important topic worth our research. The model constructed in this paper is mainly divided into two parts, one is the online learning scenario based on intelligent algorithm, and the other is the mental health monitoring model of college students. The online learning scenario provides data support for subsequent analysis, which can recommend appropriate courses for users based on users’ needs. In the psychoanalysis model, the clustering algorithm is introduced to mine the students’ online learning behavior characteristics, and then the students’ mental health is analyzed. Finally, the results show that the indicators of online learning scenarios meet the actual needs, which can provide reliable data for subsequent studies. At the same time, the students’ online learning situation is significantly related to their mental health, we can monitor the students’ psychological state in real time through the learning situation, so as to correct the students with problems in time.

The density peaks clustering (DPC) is known as an excellent approach to detect some complicated-shaped clusters with high-dimensionality. However, it is not able to detect outliers, hub nodes and boundary nodes, or form low-density clusters. Therefore, halo is adopted to improve the performance of DPC in processing low-density nodes. This paper explores the potential reasons for adopting halos instead of low-density nodes, and proposes an improved recognition method on Halo node for Density Peak Clustering algorithm (HaloDPC). The proposed HaloDPC has improved the ability to deal with varying densities, irregular shapes, the number of clusters, outlier and hub node detection. This paper presents the advantages of the HaloDPC algorithm on several test cases.

In the wake of gene-oriented data analysis in large-scale bioinformatics studies, focus in research is currently shifting towards the analysis of the functional association of genes, namely the metabolic pathways in which genes participate. The goal of this paper is to attempt to identify the core genes in a specific pathway, based on a user-defined selection of genomes. To this end, a novel algorithm has been developed that uses data from the KEGG database, and through the application of the MCL clustering algorithm, identifies clusters that correspond to different “layers” of genes, either on a phylogenetic or a functional level. The algorithm's complexity, evaluated experimentally, is presented and the results on three characteristic case studies are discussed.

This paper presents a new methodology of wear state recognition by using fractal parameters, multifractal parameters and recurrence parameters. The relationship between these nonlinear parameters is analyzed. A nonlinear state point of worn surface is established by fractal dimension, average diagonal length and spectrum width. Further, a steady state sphere is obtained by the nonlinear state point and $K$-means clustering algorithm. Results show that fractal, multifractal and recurrence parameters characterize the worn surface from different perspectives. They should be used simultaneously to comprehensively characterize the integral structures, partial structures and internal structures of worn surface. The proposed nonlinear state point shows a variation process of concentration–stabilization–separation during the wear process. The wear states can be identified effectively by the relationship between nonlinear state points and steady state sphere.

In order to solve the challenges brought by multi-source and cross-domain scenarios to online music education, this paper designs an online music education system based on advanced artificial intelligence technology, which can provide personalized learning course resource recommendations for music online learners. The system includes four layers, consisting of user interface layer, application module layer, function module layer and data storage layer. At the application module level, this paper proposes a music recommendation algorithm based on a personalized multimodal network model. The recommendation algorithm performs music information retrieval (MIR) based on the similarity judgment of the contour of music pitch and the overall change, and constructs a multimodal network model based on the user’s preference for resources to achieve personalized music recommendation. This paper crawls more than one million music score data from a well-known music platform database in China to establish a dataset to evaluate the performance of this method. The comparison results with three existing works show that the method proposed in this paper has good performance and can provide users with suitable music recommendations. The artificial intelligence technology-driven online music education mechanism proposed in this paper has good prospects.

Fuzzy clustering is a widely used approach for data classification by using the fuzzy set theory. The probability measure and the possibility measure are two popular measures which have been used in the fuzzy $c$-means algorithm (FCM) and the possibilistic clustering algorithms (PCAs), respectively. However, the numerical experiments revealed that FCM and its derivatives lack the intuitive concept of degree of belongingness, and PCAs suffer from the “coincident problem” and cannot provide very stable results for some data sets. In this study, we propose a new clustering algorithm, called the credibilistic clustering algorithm (CCA), based on the credibility measure. The credibility measure provides some unique properties which can solve the “coincident problem” and noise issue compared with the probability measure and possibility measure. Based on some randomly generated data sets, experimental results compared with FCM and PCA show that CCA can deal with the “coincident problem” with good clustering results, and it is more robust to noise than PCA.

In this paper, we present certain algorithms for clustering the vertices of fuzzy graphs(FGs) and intuitionistic fuzzy graphs(IFGs). These algorithms are based on the edge density of the given graph. We apply the algorithms to practical problems to derive the most prominent cluster among them. We also introduce parameters for intuitionistic fuzzy graphs.

In this paper we develop a measure for calculating the association coefficient between Atanassov's intuitionistic fuzzy sets (A-IFSs), and show its desirable axiomatic properties. Then we present an algorithm for clustering A-IFSs. The algorithm first utilizes the association coefficient of A-IFSs to construct an association matrix, and then calculates the λ-cutting matrix of the association matrix no matter whether it is an equivalent matrix or not. After that, the λ-cutting matrix is used to cluster A-IFSs (if the λ-cutting matrix is just only a similarity matrix, then we can easily transform it into an equivalent matrix). Three examples are used to show the effectiveness of the association coefficient and the algorithm for clustering A-IFSs. Furthermore, we extend the algorithm to cluster interval-valued intuitionistic fuzzy sets (IVIFSs), and finally, we use another numerical example to illustrate the latter algorithm.

An adaptive parallel algorithm for hierarchical clustering based on PRAM model was presented. The following approaches were devised to produce the optimized clustered data set, including the data preprocessing based on "90-10" rule to decrease the size of the data set, progressively the parallel algorithm to create Euclid minimum spanning trees on absolute graph, and the algorithm that determined the split strategies and dealt with the memory conflicts. The data set was clustered based on the noncollision memory, the lowest cost, and weakest PRAM-EREW model. N data sets were clustered in O((λn)²/p) time (0.1 ≤ λ ≤ 0.3) by performing this algorithm using p processors (1 ≤ p ≤ n/log(n)). The parallel hierarchical clustering algorithm based on PRAM model was adaptive, and of noncollision memory. The computing time could be significantly reduced after original inputting data was effectually preprocessed through the improved preprocessing methods presented in this paper.

In this paper, a novel procedure for normalising Mercer kernel is suggested firstly. Then, the normalised Mercer kernel techniques are applied to the fuzzy c-means (FCM) algorithm, which leads to a normalised kernel based FCM (NKFCM) clustering algorithm. In the NKFCM algorithm, implicit assumptions about the shapes of clusters in the FCM algorithm is removed so that the new algorithm possesses strong adaptability to cluster structures within data samples. Moreover, a new method for calculating the prototypes of clusters in input space is also proposed, which is essential for data clustering applications. Experimental results on several benchmark datasets have demonstrated the promising performance of the NKFCM algorithm in different scenarios.

We present a machine learning approach for coreference resolution of noun phrases. In our method, we use CRFs as a basic training model, and use active learning method to generate combined features so as to use existing features more effectively. We also propose a novel clustering algorithm which uses both linguistic knowledge and statistical knowledge. We build a coreference resolution system based on the proposed method and evaluate its performance from three aspects: the contributions of active learning; the effects of different clustering algorithms; and the resolution performance of different kinds of NPs. Experimental results show that additional performance gain can be obtained by using active learning method; clustering algorithm has a great effect on coreference resolution's performance and our clustering algorithm is very effective; and the key of coreference resolution is to improve the performance of the normal noun's resolution, especially the pronoun's resolution.

In this paper, we consider the problem of clustering the long financial time series of the Chinese A-share market, applying $k$-means, $k$-Shape, agglomerative hierarchical clustering, affinity propagation, and Gaussian mixture to redivide the Chinese stock market. The results after parameter tuning show that the stocks in redivided industries are more similar than those of Shenwan first-class industry. Then we generate a new method of factor neutralization, using the new industries to neutralize factors, and then constructing the investment portfolios to test the four basic factors. The experimental results show that the investment portfolio based on $k$-means can steadily defeat the benchmark and the portfolio based on classical industry classification. This new method of factor neutralization can bring a stable and effective improvement to the returns of the factors and it is allowed be applied to other factors, which has a significant impact on factor investing.

This paper proposes a distributed task allocation algorithm based on game theory to solve the complex task allocation optimization problem when UAV clusters carry heterogeneous resources and tasks have heterogeneous demands. Considering the heterogeneity of resources, two pre-processing methods are proposed: one is the grouping algorithm that combines greedy algorithm with simulated annealing algorithm, and the other is the improved K-medoids clustering algorithm based on heterogeneous resources. These pre-process methods, through grouping and clustering, can reduce the complexity of task allocation. The entropy weight method is utilized to prioritize tasks based on multiple metrics. Considering task demands, airborne resources and path cost, a coalition formation game model is established, which is proved to be a potential game. Then a distributed task allocation algorithm based on coalition formation game is designed to address the task allocation problem. Finally, the simulation involving 30 tasks with heterogeneous requirements assigned to 100 UAVs validates the effectiveness of the proposed algorithm, showing that it can achieve good task allocation results with great real-time performance.

Clustering is an important research topic and core technology in Data Mining. Clustering algorithms have been researched deeply. Now, there are lots of different clustering algorithms, they are used in special fields and users. In order to use these algorithms better, some researchers have proposed some standards to evaluate the Clustering algorithms. This paper aims to evaluate Clustering algorithms form another aspect—using the generating mixture component data sets which have overlapping phenomenon to compare Clustering algorithms' property. Based on the concept of overlap rate, we can generate data sets with different geometrical character. Then we use the data sets to evaluate Clustering algorithms to find the applicability of clustering algorithms.

The paper gives an intelligent mining sequential pattern method as a way to solve the problem of simulating Olympic temporary pots. Mining sequential patterns serves an essential role to the mining domain and offer useful resources to large number of data users. Although most mining method offer complete mining results, they simply collect and statistic all data and do not provide an effectively analytical method. This study aims to develop an intelligent mining sequential pattern method that unlike traditional statistical method uses a number of intelligent methods, including the distributed mining of sequential pattern discovery algorithm based on the prefix-projected technique and clustering algorithm, and provides more meaningful and accurate results, and at the same time improves the efficiency.

Demand side management is an effective method for conserving energy and reducing emissions. In this paper, power big data research and its application in smart grids is discussed. Using information and communications technology, a c-means clustering algorithm was made as the core, after which it was combined with cloud computing technology and an electricity demand response model to establish the constraint equation. This model was then used for a residential electricity data classification task. Taking 200 randomized sampling users, three user groups were obtained. For the first and third groups, orderly electricity consumption was carried out, and the electricity load was reduced by 5%. The calculations demonstrate the validity of our model and algorithm.