An anticipation model of potential customers’ purchasing behavior based on clustering analysis and association rules analysis

This paper proposes an anticipation model of potential customers’ purchasing behavior. This model is inferred from past purchasing behavior of loyal customers and the web server log files of loyal and potential customers by means of clustering analysis and association rules analysis. Clustering analysis collects key characteristics of loyal customers’ personal information; these are used to locate other potential customers. Association rules analysis extracts knowledge of loyal customers’ purchasing behavior, which is used to detect potential customers’ near-future interest in a star product. Despite using offline analysis to filter out potential customers based on loyal customers’ personal information and generate rules of loyal customers’ click streams based on loyal customers’ web log data, an online analysis which observes potential customers’ web logs and compares it with loyal customers’ click stream rules can more readily target potential customers who may be interested in the star products in the near future.     

Impact of a priori MS/MS intensity distributions on database search for peptide identification

Many database search methods have been developed for peptide identification throughout a large peptide data set. Most of these approaches attempt to build a decision function that allows the identification of an experimental spectrum. This function is built either starting from similarity measures for the database peptides to identify the most similar one to a given spectrum, or by applying useful learning techniques considering the database itself as a training data. In this paper, we propose a peptide identification method based on a similarity measure for peptide-spectrum matches. Our method takes into account peak intensity distribution and applies it in a probabilistic scoring model to rank peptide matches. The main goal of our approach is to highlight the relationship between peak intensities and peptide cleavage positions on the one hand and to show its impact on peptide identification on the other hand. To evaluate our method, a set of experiments have been undertaken into two high mass spectrum accuracy data sets. The obtained results show the effectiveness of our proposed approach.     

Warning system for online market research – Identifying critical situations in online opinion formation

More and more consumers are relying on online opinions when making purchasing decisions. For this reason, companies must have knowledge of the actual standing of their products on the Web. A warning system for online market research is being proposed which allows the identification of critical situations in online opinion formation. When critical situations are detected, warnings are subsequently sent to marketing managers and thus allowing marketers the ability to initiate preventive measures. The warning system operates on a knowledge base which contains product-related success values, online opinions and patterns of social interactions. This knowledge is acquired using methods coming from information extraction, text mining and social network analysis. Based on this knowledge the warning system judges situations accordingly. For this purpose, a neuro-fuzzy approach is chosen which learns linguistic rules from data. These rules are employed to estimate future situations. The warning system is applied to two scenarios and yields good results. An evaluation shows that all components of the warning system outperform alternative methods.     

A stock selection strategy using fuzzy neural networks

This paper describes, from a general system-design perspective, an artificial neural network (ANN) approach to a stock selection strategy. The paper suggests a concept of neural gates which are similar to the processing elements in ANN, but generalized into handling various types of information such as fuzzy logic, probabilistic and Boolean information together. Forecasting of stock market returns, assessing of country risk and rating of stocks based on fuzzy rules, probabilistic and Boolean data can be done using the proposed neural gates. Fuzzy logic is known to be useful for decision-making where there is a great deal of uncertainty as well as vague phenomena, but lacks the learning capability; on the other hand, neural networks are useful in constructing an adaptive system which can learn from historical data, but are not able to process ambiguous rules and probabilistic data sets. This paper describes how these problems can be solved using the proposed neural gates.     

Fuzzy logic-based forecasting model

In this paper a fuzzy logic-based software tool, fuzzy logic advisory tool (FLAT), for demand forecasting of signal transmission products is presented. The FLAT was developed for the prediction of demand of about 1000 different products in order to aid materials purchasing process of about 14,000 different components in the electronics manufacturing processes of Nokia Network Systems's Haukipudas factory. The prediction values of different products are inferred by starting from a set of eight input values. Each input value is fuzzied by the FLAT. Thereafter, fuzzy results are inferred in three sequential phases. In each phase the number of variables is split due to hierarchical structure of the inference module. A data base and a rule base are divided accordingly into three hierarchical levels. Rules are represented by linguistic relations changed into matrix equations form in order to apply linguistic equations framework technique (LE). Fuzzy membership functions for input values are determined on-line from earlier input values of the products. Fuzzy rules were inferred by analyzing behavior of the products together with market experts and product experts of the company. The model is able to produce more accurate decision-making support than more traditional approaches. This is probably due to the model-based approach and systematic data management.     

Strategic network design for reverse logistics and remanufacturing using new and old product modules

Establishment of reverse logistics (RL) networks for various original equipment manufacturers (OEM’s) is gaining significant importance. Various green legislations are forcing OEMs to take back their used, end-of-lease or end-of-life products, or products under warranty to minimize wastes and conserve resources. Therefore OEMs have turned to a better design of their products for maximum reuse and recycling and to retrieve back the used products through a network for reuse, remanufacture, recycle or disposal, so that maximum value can be achieved from their used products. However, designing of network points and assigning capacities to them depend not only on the volume of returned products but also on the demand for remanufactured products and the parts of used products. If OEMs are not able to add value to the used product, there would be no incentive to design a complex network.In this paper, a mathematical model for the design of a RL network is proposed. It is assumed that the returned products need to be consolidated in the warehouse before they are sent to reprocessing centres for inspection and dismantling. Dismantled parts are sent for remanufacturing or to the secondary market as spare parts. Recycling and disposal of these modules are also considered in the model. The use of the model is shown through its application in a numerical example.     

Feature-based recommendations for one-to-one marketing

Most recommendation systems face challenges from products that change with time, such as popular or seasonal products, since traditional market basket analysis or collaborative filtering analysis are unable to recommend new products to customers due to the fact that the products are not yet purchased by customers. Although the recommendation systems can find customer groups that have similar interests as target customers, brand new products often lack ratings and comments. Similarly, products that are less often purchased, such as furniture and home appliances, have fewer records of ratings; therefore, the chances of being recommended are often lower. This research attempts to analyze customers' purchasing behaviors based on product features from transaction records and product feature databases. Customers' preferences toward particular features of products are analyzed and then rules of customer interest profiles are thus drawn in order to recommend customers products that have potential attraction with customers. The advantage of this research is its ability of recommending to customers brand new products or rarely purchased products as long as they fit customer interest profiles; a deduction which traditional market basket analysis and collaborative filtering methods are unable to do. This research uses a two-stage clustering technique to find customers that have similar interests as target customers and recommend products to fit customers' potential requirements. Customers' interest profiles can explain recommendation results and the interests on particular features of products can be referenced for product development, while a one-to-one marketing strategy can improve profitability for companies.     

Similarity classifier using similarities based on modified probabilistic equivalence relations

This paper examines a classifier based on similarity measures originating from probabilistic equivalence relations with a generalized mean. Equivalences are weighted and weight optimization is carried out with differential evolution algorithms. In the classifier, a similarity measure based on the ?ukasiewicz structure has previously been used, but this paper concentrates on measures which can be considered to be weighted similarity measures defined in a probabilistic framework, applied variable by variable and aggregated along the features using a generalized mean. The weights for these measures are determined using a differential evolution process. The classification accuracy with these measures are tested on different data sets. Classification results are obtained with medical data sets, and the results are compared to other classifiers, which gives quite good results. The result presented in this paper are promising, and in several cases better results were achieved.     

A comparison of two MLEM2 rule induction algorithms extended to probabilistic approximations

A probabilistic approximation is a generalization of the standard idea of lower and upper approximations, defined for equivalence relations. Recently probabilistic approximations were additionally generalized to an arbitrary binary relation so that probabilistic approximations may be applied for incomplete data. We discuss two ways to induce rules from incomplete data using probabilistic approximations, by applying true MLEM2 algorithm and an emulated MLEM2 algorithm. In this paper we report novel research on a comparison of both approaches: new results of experiments on incomplete data with three interpretations of missing attribute values. Our results show that both approaches do not differ much.     

Algebraic reasoning for probabilistic action systems and while-loops

Back and von Wright have developed algebraic laws for reasoning about loops in a total correctness framework using the refinement calculus. We extend their work to reasoning about probabilistic loops in the probabilistic refinement calculus. We apply our algebraic reasoning to derive transformation rules for probabilistic action systems and probabilistic while-loops. In particular we focus on developing data refinement rules for these two constructs. Our extension is interesting since some well known transformation rules that are applicable to standard programs are not applicable to probabilistic ones: we identify some of these important differences and we develop alternative rules where possible.     

Vague集之间相似性度量的基本准则与一般方法

研究一般情形下Vague集之间的相似性度量问题。分析现有相似度度量方法的种类并指出其不足之处,提出Vague值间相似性度量的基本准则。考虑到未知信息对相似度的影响,根据基本准则提出适用于一般情况的度量公式。讨论该公式满足基本准则的必要条件,并给出具体的公式。最后通过模式识别中的实例说明其有效性。     

A dynamic decision support system to predict the value of customer for new product development

In recent years, firms have focused on how to enter markets and meet customer requirements by improving product attributes and processes to boost their market share and profits. Consequently, market-driven product design and development has become a popular topic in the literature. However, past research neither covers all of the major influencing factors that together drive customers to make purchase decisions, nor connects these various influencing factors to customer purchasing behavior. Past studies further fail to take the time value of money and customer value into consideration. This study proposes a decision support system to (a) predict customer purchasing behavior given certain product, customer, and marketing influencing factors, and (b) estimate the net customer lifetime value from customer purchasing behavior toward a specific product. This will not only enable decision-makers to compare alternatives and select competitive products to launch on the market, but will also improve the understanding of customer behavior toward particular products for the formulation of effective marketing strategies that increase customer loyalty and generate greater profits in the long term. Decision-makers can also make use of the system to build up confidence in new product development in terms of idea generation and product improvement. The application of the proposed system is illustrated and confirmed to be sensible and convincing through a case study.     

Learning fuzzy rules for similarity assessment in case-based reasoning

Fundamental to case-based reasoning is the assumption that similar problems have similar solutions. The meaning of the concept of “similarity” can vary in different situations and remains an issue. This paper proposes a novel similarity model consisting of fuzzy rules to represent the semantics and evaluation criteria for similarity. We believe that fuzzy if-then rules present a more powerful and flexible means to capture domain knowledge for utility oriented similarity modeling than traditional similarity measures based on feature weighting. Fuzzy rule-based reasoning is utilized as a case matching mechanism to determine whether and to which extent a known case in the case library is similar to a given problem in query. Further, we explain that such fuzzy rules for similarity assessment can be learned from the case library using genetic algorithms. The key to this is pair-wise comparisons of cases with known solutions in the case library such that sufficient training samples can be derived for genetic-based fuzzy rule learning. The evaluations conducted have shown the superiority of the proposed method in similarity modeling over traditional schemes as well as the feasibility of learning fuzzy similarity rules from a rather small case base while still yielding competent system performance.     

Exploring consumer adoption of new services by analyzing the behavior of 3G subscribers: An empirical case study

As the profit margins of 3G mobile network operators gradually decline, and market competition becomes increasingly intensive, they must develop rich and diverse varieties of brand new application services to attract new subscribers and retain old ones. Understanding the customer’s purchasing behavior is a key issue in this process. The operator must accurately grasp movements in the market based on analysis of the behavior of 3G subscribers. This study proposes a comprehensive customer relationship management strategy framework to furnish a beneficial plan to overcome such challenges. First, we propose a new model to identify who are the high-value customers related to the characteristics of new telecommunication services. After segmenting the customers, we propose a procedure to provide different kinds of usage analysis, including inter-cluster analysis and intra-cluster analysis. The experimental results are determined based on rules extracted from a large number of call detail records generated by the mobile subscribers of leading 3G mobile system operators in Taiwan. The dependency network demonstrates the relationship between voice services, data communications, message services, micropayments and entertainment. Finally, we propose some marketing recommendations for 3G system operators based on these interesting rules.     

Efficient and effective similarity search over probabilistic data based on Earth Mover’s Distance

Advances in geographical tracking, multimedia processing, information extraction, and sensor networks have created a deluge of probabilistic data. While similarity search is an important tool to support the manipulation of probabilistic data, it raises new challenges to traditional relational databases. The problem stems from the limited effectiveness of the distance metrics employed by existing database systems. On the other hand, several more complicated distance operators have proven their values for better distinguishing ability in specific probabilistic domains. In this paper, we discuss the similarity search problem with respect to Earth Mover’s Distance (EMD). EMD is the most successful distance metric for probability distribution comparison but is an expensive operator as it has cubic time complexity. We present a new database indexing approach to answer EMD-based similarity queries, including range queries and k-nearest neighbor queries on probabilistic data. Our solution utilizes primal-dual theory from linear programming and employs a group of B ⁺ trees for effective candidate pruning. We also apply our filtering technique to the processing of continuous similarity queries, especially with applications to frame copy detection in real-time videos. Extensive experiments show that our proposals dramatically improve the usefulness and scalability of probabilistic data management.     

Inclusion measures of probabilistic linguistic term sets and their application in classifying cities in the Economic Zone of Chengdu Plain

The probabilistic linguistic term set is a powerful tool to express and characterize people’s cognitive complex information and thus has obtained a great development in the last several years. To better use the probabilistic linguistic term sets in decision making, information measures such as the distance measure, similarity measure, entropy measure and correlation measure should be defined. However, as an important kind of information measure, the inclusion measure has not been defined by scholars. This study aims to propose the inclusion measure for probabilistic linguistic term sets. Formulas to calculate the inclusion degrees are put forward Then, we introduce the normalized axiomatic definitions of the distance, similarity and entropy measures of probabilistic linguistic term sets to construct a unified framework of information measures for probabilistic linguistic term sets. Based on these definitions, we present the relationships and transformation functions among the distance, similarity, entropy and inclusion measures. We believe that more formulas to calculate the distance, similarity, inclusion degree and entropy can be induced based on these transformation functions. Finally, we put forward an orthogonal clustering algorithm based on the inclusion measure and use it in classifying cities in the Economic Zone of Chengdu Plain, China.     

一种基于马尔科夫网的本体匹配算法

概率模型是解决不确定性推理和数据分析的有效工具。针对本体匹配的不确定性,提出一种基于马尔科夫网的本体匹配改进算法。采用多种传统匹配算法计算相似度矩阵,改进相似度传播规则,添加2种结构稳定性约束规则和1种Disjoint一致性约束规则,定义其对应团的势函数。根据相似度矩阵和上述规则,给出马尔科夫网的构造方法,使用循环置信度传播算法计算随机变量的后验概率,依据后验概率得到最后的本体匹配结果。在OAEI2010数据集上进行实验,结果表明,与iMatch本体匹配系统相比,该算法能有效降低概率模型的复杂度,提高本体匹配的准确率和召回率。     

外包、自研还是交叉授权?-----OEM互补性技术策略选择

考虑处于市场竞争的两个OEM的互补性技术策略选择问题,每个OEM只掌握一种互补性技术,且二者研发能力存在异质性,而产品的生产需两种互补性技术的结合.针对各自缺乏的技术,构建OEM的外包、自研和交叉授权3种技术策略选择模型,通过比较3种情形下的均衡结果,识别出OEM的最优技术策略选择.研究表明:具有技术优势的OEM进行技术外包时,始终存在创新抑制,然而如果它拥有极强的研发能力,外包比自研更有利;当强势方的授权程度较低并且弱势方授权程度适中时,两个OEM偏好独立研发,将放弃交叉授权;相比技术外包,技术领先的OEM对交叉授权的态度更积极,反之亦然;OEM的技术策略偏好取决于技术研发能力差异和交叉授权效应,仅有自研或交叉授权可能成为OEM的共同最优策略.     

Mining shopping behavior in the Taiwan luxury products market

The rapid growth of Taiwan’s economy has been accompanied by the country’s developing market for luxury products. To successfully establish the new market demand chain for the luxury industry in Taiwan, it is essential to understand customer preferences. Thus, this study uses an association rules approach and clustering analysis for data mining to mine knowledge among luxury product-buying customers in Taiwan. The results of knowledge extraction from data mining, illustrated as knowledge patterns, rules and knowledge maps, are used to make recommendations for future developments in the luxury products industry.