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K-nearest neighbors search (K-NNS) is a fundamental problem in many areas of machine learning and data mining. In an attempt to solve NNS problems by locality-sensitive hashing (LSH)-based algorithms and avoid the F1-trap, in this paper, we theoretically prove the necessity of maintaining close edges of each dimension, the independence of the partition location and the precision of retrieval. Based on the theories, a new method is proposed for constructing the hash function family. Then, a volume-balancing locality-sensitive hashing (VBLSH) algorithm is implemented. In this algorithm, the problem of constructing the hash function family is transformed into an optimization problem under a fixed number of hypercubes. By solving the optimization problem, different hyperplane intervals are set for each dimension to ensure that the hash functions can evenly segment the data space, where each segmented hypercube has a close volume and close side lengths. Finally, the analysis of the algorithm shows that the VBLSH algorithm has a high index coding efficiency and hash code calculation efficiency. The experimental results show that the VBLSH algorithm can simultaneously take into account the efficiency, precision and recall rate, and avoid the F1-trap; it shows excellent effectiveness on 4 datasets. [ABSTRACT FROM AUTHOR]

Data clustering aims to segment a database into groups of objects based on the similarity among these objects. Due to its unsupervised nature, the search for a good-quality solution can become a complex process. There is currently a wide range of clustering algorithms, and selecting the best one for a given problem can be a slow and costly process. In 1976, Rice formulated the Algorithm Selection Problem (ASP), which postulates that the algorithm performance can be predicted based on the structural characteristics of the problem. Meta-learning brings the concept of learning about learning; that is, the meta-knowledge obtained from the algorithm learning process allows the improvement of the algorithm performance. Meta-learning has a major intersection with data mining in classification problems, in which it is normally used to recommend algorithms. The present paper proposes new ways to obtain meta-knowledge for clustering tasks. Specifically, two contributions are explored here: (1) a new approach to characterize clustering problems based on the similarity among objects; and (2) new methods to combine internal indices for ranking algorithms based on their performance on the problems. Experiments were conducted to evaluate the recommendation quality. The results show that the new meta-knowledge provides high-quality algorithm selection for clustering tasks. [ABSTRACT FROM AUTHOR]