A novel stock trading prediction and recommendation system

The prices of the stock are influence by many factors and emerge extremely nonlinear structure. Therefore, the stock trading prediction and recommendation is an extremely challenging task. In this paper, a novel stock trading prediction and recommendation system is proposed in user-friendly form. The recommendation system can inform the user whether is to buy or sell the stocks in the next step. Information granulation is applied to transform raw time series into meaningful and interpretable granules, and the more effective non-uniform partitioning method for prediction is presented. The system first determines the intervals based on information granules, and then define the fuzzy sets and fuzzify the historical data. Third, construct fuzzy relationships and assign weights to each period. Finally, the prediction and recommendation is implemented. The experimental results show the proposed system yields better prediction performance, and increases profit-making opportunities.     

A novel stock recommendation system using Guba sentiment analysis

Investment recommendation has been one of the hottest topics in the finance area which can help investors to get more profits and to avoid loss. Existing recommendation systems mostly depend on analysis of trading data and company profit prediction. Though many works show that there is a positive correlation between investors’ sentiment and the finance market trends, few recommendation theories have been built based on sentiment. The primary reason is the difficulty to measure investors’ sentiment. In this work, a novel stock recommendation system is developed based on a proposed theory concerning the correlation between Guba-based sentiment of the retail investors and the stock market trends in China. To verify four hypotheses of the theory, a novel method is proposed to measure the investors’ sentiment by exploiting the large volumes of emotion enriched texts posted in Guba, which is online social platform for individual investors to share news and opinions concerning their favorite stocks. Results show the correctness of the proposed theory: (1) there is a positive correlation between Guba-based sentiment and the stock market trends; 2) the higher the post volumes and agreement, more proficiency the bullishness would be; and (3) a long-lasting negative Guba-based sentiment indicates the arrival of the bear market. The proposed recommendation system consists of three criteria accordingly to ensure the portfolio to meet requirements of the theory. Finally, experiments are implemented using the real data of Chinese stock market from March 2009 to March 2016 and the results show the effectiveness of the proposed system in recommending lucrative stocks and the theoretical cumulate profit is about eight times of the CSI300 in the period.     

Empirical study of trading rule discovery in China stock market

This case study employs classic knowledge engineering methods. We found a new template grid from Shanghai Stock Exchange, and compared it with the classic one. The results show that the new template grid is more effective than the classic one. Then, we built a trading rule named Rule 1v. The finding of results using our trading rule, which is significantly better than the overall average for the period, constitute a failure to confirm our implicit null hypothesis, which is the efficient market hypothesis.     

Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review

Despite the wide application of evolutionary computation (EC) techniques to rule discovery in stock algorithmic trading (AT), a comprehensive literature review on this topic is unavailable. Therefore, this paper aims to provide the first systematic literature review on the state-of-the-art application of EC techniques for rule discovery in stock AT. Out of 650 articles published before 2013 (inclusive), 51 relevant articles from 24 journals were confirmed. These papers were reviewed and grouped into three analytical method categories (fundamental analysis, technical analysis, and blending analysis) and three EC technique categories (evolutionary algorithm, swarm intelligence, and hybrid EC techniques). A significant bias toward the applications of genetic algorithm-based (GA) and genetic programming-based (GP) techniques in technical trading rule discovery is observed. Other EC techniques and fundamental analysis lack sufficient study. Furthermore, we summarize the information on the evaluation scheme of selected papers and particularly analyze the researches which compare their models with buy and hold strategy (B&H). We observe an interesting phenomenon where most of the existing techniques perform effectively in the downtrend and poorly in the uptrend, and considering the distribution of research in the classification framework, we suggest that this phenomenon can be attributed to the inclination of factor selections and problem in transaction cost selections. We also observe the significant influence of the transaction cost change on the margins of excess return. Other influenced factors are also presented in detail. The absence of ways for market trend prediction and the selection of transaction cost are two major limitations of the studies reviewed. In addition, the combination of trading rule discovery techniques and portfolio selection is a major research gap. Our review reveals the research focus and gaps in applying EC techniques for rule discovery in stock AT and suggests a roadmap for future research.     

Privacy preservation for recommendation databases

Since recommendation systems play an important role in the current situations where such digital transformation is highly demanded, the privacy of the individuals’ collected data in the systems must be secured effectively. In this paper, the vulnerability of the existing query framework for the recommendation systems is identified. Thus, we propose to apply the well-known k-anonymity model to generalize the given recommendation databases to satisfy the privacy preservation constraint. We show that such data generalization problem which minimizes the impact on data utility is NP-hard. To tackle with such problem, an algorithm to preserve the privacy of the individuals in the recommendation databases is proposed. The idea is to avoid excessive generalizing on the databases by forming a group of similar tuples in the databases. Thus, the impact on the data utility of the generalizing such group can be minimized. Our work is evaluated by extensive experiments. From the results, it is found that our work is highly effective, i.e., the impact quantified by the data utility metrics and the errors of the query results are less than the compared algorithms, and also it is highly efficient, i.e., the execution time is less than the result of its effectiveness-comparable algorithm by more than three times.     

基于改进规则引擎的农业知识推荐系统

为了使农民更加高效的获取农业知识,提出了一种基于改进规则引擎的农业知识系统.通过深入研究规则引擎的工作原理,将其引入到农业知识推荐系统中,增加了系统的正确性和准确性,使系统能够在最恰当的时间向农民提供他们最希望得到的正确的农业知识.同时根据农业领域的特点对规则引擎进行了改进,提出了规则库的树形结构化,并采用规则文件的运行前编译方式.将改进后的规则引擎应用于农业知识推荐系统中,提高了系统的效率.     

A new student performance analysing system using knowledge discovery in higher educational databases

Knowledge discovery is a wide ranged process including data mining, which is used to find out meaningful and useful patterns in large amounts of data. In order to explore the factors having impact on the success of university students, knowledge discovery software, called MUSKUP, has been developed and tested on student data. In this system a decision tree classification is employed as a data mining technique. With this software system all the tasks involved in the knowledge discovery process are kept together. The advantage of this approach is to have access to all the functionalities of SQL server and Analysis Services through single software. The study was carried out on the data from university students. According to results of the study, the types of registration to the university and the income levels of the students’ family were found to be associated with student success.     

A stock market portfolio recommender system based on association rule mining

We propose a stock market portfolio recommender system based on association rule mining (ARM) that analyzes stock data and suggests a ranked basket of stocks. The objective of this recommender system is to support stock market traders, individual investors and fund managers in their decisions by suggesting investment in a group of equity stocks when strong evidence of possible profit from these transactions is available.Our system is different compared to existing systems because it finds the correlation between stocks and recommends a portfolio. Existing techniques recommend buying or selling a single stock and do not recommend a portfolio.We have used the support confidence framework for generating association rules. The use of traditional ARM is infeasible because the number of association rules is exponential and finding relevant rules from this set is difficult. Therefore ARM techniques have been augmented with domain specific techniques like formation of thematical sectors, use of cross-sector and intra-sector rules to overcome the disadvantages of traditional ARM.We have implemented novel methods like using fuzzy logic and the concept of time lags to generate datasets from actual data of stock prices.Thorough experimentation has been performed on a variety of datasets like the BSE-30 sensitive Index, the S&P CNX Nifty or NSE-50, S&P CNX-100 and DOW-30 Industrial Average. We have compared the returns of our recommender system with the returns obtained from the top-5 mutual funds in India. The results of our system have surpassed the results from the mutual funds for all the datasets.Our approach demonstrates the application of soft computing techniques like ARM and fuzzy classification in the design of recommender systems.     

A graphical user-interface for knowledge discovery in databases

This paper describes a graphical user-interface for database-oriented knowledge discovery systems, DBLEARN, which has been developed for extracting knowledge rules from relational databases. The interface, designed using a query-by-example approach, provides a graphical means of specifying knowledge-discovery tasks. The interface supplies a graphical browsing facility to help users to perceive the nature of the target database structure. In order to guide users' task specification, a cooperative, menu-based guidance facility has been integrated into the interface. The interface also supplies a graphical interactive adjusting facility for helping users to refine the task specification to improve the quality of learned knowledge rules.     

A virtual mart for knowledge discovery in databases

The Web has profoundly reshaped our vision of information management and processing, enlightening the power of a collaborative model of information production and consumption. This new vision influences the Knowledge Discovery in Databases domain as well. In this paper we propose a service-oriented, semantic-supported approach to the development of a platform for sharing and reuse of resources (data processing and mining techniques), enabling the management of different implementations of the same technique and characterized by a community-centered attitude, with functionalities for both resource production and consumption, facilitating end-users with different skills as well as resource providers with different technical and domain specific capabilities. We first describe the semantic framework underlying the approach, then we demonstrate how this framework is exploited to give different functionalities to users through the presentation of the platform functionalities.     

Using the moving average rule in a dynamic web recommendation system

In this, the Information Age, most people are accustomed to gleaning information from the World Wide Web. To survive and prosper, a Web site has to constantly enliven its content while providing various and extensive information services to attract users. The Web Recommendation System, a personalized information filter, prompts users to visit a Web site and browse at a deeper level. In general, most of the recommendation systems use large browsing logs to identify and predict users' surfing habits. The process of pattern discovery is time-consuming, and the result is static. Such systems do not satisfy the end users' goal-oriented and dynamic demands. Accordingly, a pressing need for an adaptive recommendation system comes into play. This article proposes a novel Web recommendation system framework, based on the Moving Average Rule, which can respond to new navigation trends and dynamically adapts recommendations for users with suitable suggestions through hyperlinks. The framework provides Web site administrators with various methods to generate recommendations. It also responds to new Web trends, including Web pages that have been updated but have not yet been integrated into regular browsing patterns. Ultimately, this research enables Web sites with dynamic intelligence to effectively tailor users' needs. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 621–639, 2007.     

Abstract-driven pattern discovery in databases

The problem of discovering interesting patterns in large volumes of data is studied. Patterns can be expressed not only in terms of the database schema but also in user-defined terms, such as relational views and classification hierarchies. The user-defined terminology is stored in a data dictionary that maps it into the language of the database schema. A pattern is defined as a deductive rule expressed in user-defined terms that has a degree of uncertainty associated with it. Methods are presented for discovering interesting patterns based on abstracts which are summaries of the data expressed in the language of the user     

Revisable knowledge discovery in databases

This article introduces the idea of using nonmonotonic inheritance networks for the storage and maintenance of knowledge discovered in data (revisable knowledge discovery in databases). While existing data mining strategies for knowledge discovery in databases typically involve initial structuring through the use of identification trees and the subsequent extraction of rules from these trees for use in rule-based expert systems, such strategies have difficulty in coping with additional information which may conflict with that already used for the automatic generation of rules. In the worst case, the entire automatic sequence may have to be repeated. If nonmonotonic inheritance networks are used instead of rules for storing knowledge discovered in databases, additional conflicting information can be inserted directly into such structures, thereby bypassing the need for recompilation. © 1996 John Wiley & Sons, Inc.     

Knowledge discovery in molecular databases

An approach to knowledge discovery in complex molecular databases is described. The machine learning paradigm used is structured concept formation, in which object's described in terms of components and their interrelationships are clustered and organized in a knowledge base. Symbolic images are used to represent classes of structured objects. A discovered molecular knowledge base is successfully used in the interpretation of a high resolution electron density map     

PatchIndex: exploiting approximate constraints in distributed databases

Distributed and Parallel Databases - Cloud data warehouse systems lower the barrier to access data analytics. These applications often lack a database administrator and integrate data from various...     

A framework for knowledge discovery and evolution in databases

A concept for knowledge discovery and evolution in databases is described. The key issues include: using a database query to discover new rules; using not only positive examples (answer to a query), but also negative examples to discover new rules; and harmonizing existing rules with the new rules. A tool for characterizing the exceptions in databases and evolving knowledge as a database evolves is developed     

数据库中的模糊逻辑规则发现

关联规则是数据库中的知识发现（KDD）领域的重要研究课题。模糊关联规则可以用自然语言来表达人类知识,近年来受到KDD研究人员的普遍关注。但是,目前大多数模糊关联规则发现方法仍然沿用经典关联规则发现中常用的支持度和置信度测度。事实上,模糊关联规则可以有不同的解释,而且不同的解释对规则发现方法有很大影响。从逻辑的观点出发,定义了模糊逻辑规则、支持度、蕴含度及其相关概念,提出了模糊逻辑规则发现算法,该算法结合了模糊逻辑概念和Apriori算法,从给定的定量数据库中发现模糊逻辑规则。     

Systems for knowledge discovery in databases

Knowledge-discovery systems face challenging problems from real-world databases, which tend to be dynamic, incomplete, redundant, noisy, sparse, and very large. These problems are addressed and some techniques for handling them are described. A model of an idealized knowledge-discovery system is presented as a reference for studying and designing new systems. This model is used in the comparison of three systems: CoverStory, EXPLORA, and the Knowledge Discovery Workbench. The deficiencies of existing systems relative to the model reveal several open problems for future research     

Knowledge discovery in databases using lattices

The rapid pace at which data gathering, storage and distribution technologies are developing is outpacing our advances in techniques for helping humans to analyse, understand, and digest the vast amounts of resulting data. This has led to the birth of knowledge discovery in databases (KDD) and data mining—a process that has the goal to selectively extract knowledge from data. A range of techniques, including neural networks, rule-based systems, case-based reasoning, machine learning, statistics, etc. can be applied to the problem. We discuss the use of concept lattices, to determine dependences in the data mining process. We first define concept lattices, after which we show how they represent knowledge and how they are formed from raw data. Finally, we show how the lattice-based technique addresses different processes in KDD, especially visualization and navigation of discovered knowledge.     

Knowledge discovery in time series databases

Adding the dimension of time to databases produces time series databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. In this correspondence, we introduce a general methodology for knowledge discovery in TSDB. The process of knowledge discovery in TSDR includes cleaning and filtering of time series data, identifying the most important predicting attributes, and extracting a set of association rules that can be used to predict the time series behavior in the future. Our method is based on signal processing techniques and the information-theoretic fuzzy approach to knowledge discovery. The computational theory of perception (CTP) is used to reduce the set of extracted rules by fuzzification and aggregation. We demonstrate our approach on two types of time series: stock-market data and weather data.