Cluster analysis is an important method in data mining and has been widely used in various aspects of real life. The Fuzzy K-Modes algorithm is one of the most commonly used algorithms for clustering categorical data due to its simplicity and effectiveness. However, this algorithm has three main problems: sensitivity of initial cluster centers, vulnerability of distance measurement methods to noise, and failure to consider different contributions of attributes during the clustering process. In this paper, we overview some existing improvement algorithms based on the three problems in detail and conduct experimental analysis of these algorithms on real data sets. Furthermore, we summarize the challenges faced in the process of improving the Fuzzy K-Modes algorithm and give possible research directions in future.
With the continuous increase of Internet information, the traditional single-hop query is not enough to meet the needs of users. For complex problems, multi-hop KGQA requires reasoning at multiple edges of the KG to arrive at the correct answer. KGS often lack many links, which poses additional challenges for KGQAs especially multi-jump KGQAs. In this paper, the main multi-hop question answering algorithms are divided into two categories: embedded-based multi-hop knowledge question answering reasoning and linked multi-hop knowledge question answering reasoning. The results show that the EmbedKGQA model performs better in prediction reasoning under the knowledge map with missing links by analyzing and comparing the performance of the subgraph matching and embedding prediction models on the Meta and WebQestionSP data sets. Finally, in view of the absence of knowledge graph data in practical application, we propose the development prospect of knowledge graph multi-hop algorithm from the two directions of combining pre-training with knowledge graph and using multi-modal model to expand the data in knowledge graph from multiple dimensions of the same entity.
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