## 1 Introduction  
With the development of information technologies such as big data (BD), business intelligence (BI), and artificial intelligence (AI), the scopes of business for many companies have changed significantly. These developments have had a great impact on the strategies for traditional industries, as some new business scopes have become of interest. An interesting discovery is that although some companies share the same business scope, the industries involved vary. These findings have led researchers to focus more attention on business scopes, relegating important issues of participation to the background.  
As computer science continues to evolve, it becomes meaningful to examine the changes taking place in the scopes of interest for businesses. As some works claim, a review of the daily news or annual financial reports is a reasonable approach to this. Findings will help the U.S. government to publish policies more closely aligned with demands of markets. Founders or operators of companies may gage areas of growth and potential needs for their business development planning based on these analyses. Finally, investors could use this data to base their investment choices on companies which have promising business scopes.  
Google Finance makes rough judgments of whether two companies shared the same or similar business scopes. This judgment is made by domain experts. In theory, news text and expert judgments like those of Google Finance would reveal whether two companies have the same business scope. However, the influence of this scope remains uncertain. Time series comparison is a helpful method to complete the process of judgments, as researchers could carry out the clustering work of companies in a more convincing way by combining time-series data and text data.  
From 2012 to 2017, popular business scopes were constantly changing with the development of new digital concepts. A significant issue is to identify the promising business scopes for the current U.S. market. To provide a reasonable approach to understanding the trends of the American market, this article uses sophisticated research methodology to identify such trends, with more processed data than prior studies. In this paper, the authors mainly focus on the companies in the New York Stock Exchange (NYSE) and the National Association of Securities Dealers Automated Quotations (NASDAQ). This article aimed to solve the following three questions:  
1. What kind of business scopes are popular in the current U.S. market?
2. What key factors or specific productions show a promising development trend?
3. What business scopes are in alignment with U.S. market trends?  
In this paper, authors offer a concurrent text and time series active learning algorithm to carry out the clustering work. This algorithm shows three main advantages compared to existing methods:  
1. Use of text similarity to adjust the clustering results of numerical data, improving the clustering accuracy. Text similarity is used to evaluate the effectiveness of the clustering algorithm in most existing methods. In this paper, text similarity is directly used as a clustering index to carry out clustering work.
2. Active learning is introduced in the factor weight assignment stage. This method implies lower manpower costs, with lower time complexity.
3. Under this framework, more similarity measures can be introduced so that the clustering results more closely represent the actual situation, providing a better guiding effect with respect to the U.S. stock market.  
The authors contend that the findings in this paper may provide a more comprehensive understanding of the current U.S. market in business scopes.