Different types of data, needs of users and variety application problems are lead to produce a range of methods to discover patterns and dependent relationships. This application follows a set of association rules according to know which one of set of objects affects on a set of other objects. This association rules predict the occurrence of an object based on the occurrence of other objects. The associative algorithms have the challenge of redundant association rules and patterns, but studying various methods of association rules is expressive that the recent researches focused on solving the challenges of the tree and lattice structures and their compounds about association algorithms. In this paper, the associative algorithms and their function are described, and finally the new improved association algorithms and the proposed solutions to solve these challenges are explained.

Clustering is one of the data mining methods. In all clustering algorithms, the goal is to minimize intracluster distances, and to maximize intercluster distances. Whatever a clustering algorithm provides a better performance, it has the more successful to achieve this goal. Nowadays, although many research done in the field of clustering algorithms, these algorithms have the challenges such as processing time, scalability, accuracy, etc. Comparing various methods of the clustering, the contributions of the recent researches focused on solving the clustering challenges of the partition method. In this paper, the partitioning clustering method is introduced, the procedure of the clustering algorithms is described, and finally the new improved methods and the proposed solutions to solve these challenges are explained.

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Fuzzy VIKOR C-means (FVCM) is a kind of unsupervised fuzzy clustering algorithm that improves the accuracyand computational speed of Fuzzy C-means (FCM). So it reduces the sensitivity to noisy and outlier data, and enhances performance and quality of clusters. Since FVCM allocates some data to a specific cluster based on similarity technique, reducing the effect of noisy data increases the quality of the clusters. This paper presents a new approach to the accurate location of noisy data to the clusters overcoming the constraints of noisy points through fuzzy support vector machine (FSVM), called FVCM-FSVM, so that at each stage samples with a high degree of membership are selected for training in the classification of FSVM. Then, the labels of the remaining samples are predicted so the process continues until the convergence of the FVCM-FSVM. The results of the numerical experiments showed the proposed approach has better performance than FVCM. Of course, it greatly achieves high accuracy.