Competitor identification is an essential component of corporate strategy. With the rapid development of artificial intelligence, various data-mining methodologies and frameworks have emerged to identify competitors. In general, the competitiveness among companies is determined by both market commonality and resource similarity. However, because resource information is more difficult to obtain than market information, existing studies primarily identify competitors via market commonality. To address this limitation, we introduce multisource company descriptions as well as heterogeneous business relationships, and we propose a novel method for simultaneously mining the market commonality and resource similarity. First, we use multisource company descriptions to represent companies and transform the heterogeneous business relationships into a heterogeneous business network. Then, we propose a novel multisource data fusion-based heterogeneous graph attention network (MHGAT) to learn the pairwise competitive relationships between companies. Specifically, a graph neural network-based model is proposed to learn the embeddings of companies by preserving their competition, and a multilevel attention framework is designed to integrate the embeddings from neighboring company level, heterogeneous relationship level, and multisource description level. Finally, experiments on a real-world dataset verify the effectiveness of our proposed MHGAT and demonstrate the usefulness of company descriptions and business relationships in competitor identification.

Dominance-based rough sets approach (DRSA) is an effective tool to deal with information with preference-ordered attribute domains and decision classes. Any information system may evolve when new objects enter into or old objects get out. Approximations of DRSA need update for decision analysis or other relative tasks. Incremental updating is a feasible and effective technique to update approximations. The purpose of this paper is to present an incremental approach for updating approximations of DRSA. The approach is applicable to dynamic information systems when the set of objects varies over time. In this paper, we discuss the principles of incrementally updating P-dominating sets and P-dominated sets and propose an incremental approach for updating approximations of DRSA. A numerical example is given to illustrate the incremental approach. The experimental evaluations on data sets from UCI show that the incremental approach outperforms the original nonincremental one.

Approximations of a concept in rough set theory induce rules and need to update for dynamic data mining and related tasks. Most existing incremental methods based on the classical rough set model can only be used to deal with the categorical data. This paper presents a new dynamic method for incrementally updating approximations of a concept under neighborhood rough sets to deal with numerical data. A comparison of the proposed incremental method with a nonincremental method of dynamic maintenance of rough set approximations is conducted by an extensive experimental evaluation on different data sets from UCI. Experimental results show that the proposed method effectively updates approximations of a concept in practice. © 2012 Wiley Periodicals, Inc.