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Domain-Driven Data Mining: Challenges and Prospects
"... Abstract—Traditional data mining research mainly focus]es on developing, demonstrating, and pushing the use of specific algorithms and models. The process of data mining stops at pattern identification. Consequently, a widely seen fact is that 1) many algorithms have been designed of which very few ..."
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Abstract—Traditional data mining research mainly focus]es on developing, demonstrating, and pushing the use of specific algorithms and models. The process of data mining stops at pattern identification. Consequently, a widely seen fact is that 1) many algorithms have been designed of which very few are repeatable and executable in the real world, 2) often many patterns are mined but a major proportion of them are either commonsense or of no particular interest to business, and 3) end users generally cannot easily understand and take them over for business use. In summary, we see that the findings are not actionable, and lack soft power in solving real-world complex problems. Thorough efforts are essential for promoting the actionability of knowledge discovery in realworld smart decision making. To this end, domain-driven data mining (D 3 M) has been proposed to tackle the above issues, and promote the paradigm shift from “data-centered knowledge discovery ” to “domain-driven, actionable knowledge delivery. ” In D 3 M, ubiquitous intelligence is incorporated into the mining process and models, and a corresponding problem-solving system is formed as the space for knowledge discovery and delivery. Based on our related work, this paper presents an overview of driving forces, theoretical frameworks, architectures, techniques, case studies, and open issues of D 3 M. We understand D 3 M discloses many critical issues with no thorough and mature solutions available for now, which indicates the challenges and prospects for this new topic. Index Terms—Data mining, domain-driven data mining (D 3 M), actionable knowledge discovery and delivery. Ç 1
Combined mining: Discovering informative knowledge in complex data
- Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
, 2011
"... Abstract—Enterprise data mining applications often involve complex data such as multiple large heterogeneous data sources, user preferences, and business impact. In such situations, a single method or one-step mining is often limited in discovering informa-tive knowledge. It would also be very time ..."
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Abstract—Enterprise data mining applications often involve complex data such as multiple large heterogeneous data sources, user preferences, and business impact. In such situations, a single method or one-step mining is often limited in discovering informa-tive knowledge. It would also be very time and space consuming, if not impossible, to join relevant large data sources for mining patterns consisting of multiple aspects of information. It is crucial to develop effective approaches for mining patterns combining necessary information from multiple relevant business lines, cater-ing for real business settings and decision-making actions rather than just providing a single line of patterns. The recent years have seen increasing efforts on mining more informative patterns, e.g., integrating frequent pattern mining with classifications to gener-ate frequent pattern-based classifiers. Rather than presenting a specific algorithm, this paper builds on our existing works and proposes combined mining as a general approach to mining for informative patterns combining components from either multiple data sets or multiple features or by multiple methods on de-mand. We summarize general frameworks, paradigms, and basic processes for multifeature combined mining, multisource combined mining, and multimethod combined mining. Novel types of com-bined patterns, such as incremental cluster patterns, can result from such frameworks, which cannot be directly produced by the existing methods. A set of real-world case studies has been con-ducted to test the frameworks, with some of them briefed in this paper. They identify combined patterns for informing government debt prevention and improving government service objectives, which show the flexibility and instantiation capability of combined mining in discovering informative knowledge in complex data. Index Terms—Actionable knowledge discovery, combined min-ing, complex data, data mining, multiple source data mining, public service data mining. I.
Combined mining: Analyzing object and pattern relations for discovering and constructing complex yet actionable patterns. WIREs Data Mining and Knowledge Discovery
"... Combined mining is a technique for analyzing object relations and pattern re-lations, and for extracting and constructing actionable complex knowledge (pat-terns or exceptions) in complex situations. Although combined patterns can be built within a single method, such as combined sequential patterns ..."
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Combined mining is a technique for analyzing object relations and pattern re-lations, and for extracting and constructing actionable complex knowledge (pat-terns or exceptions) in complex situations. Although combined patterns can be built within a single method, such as combined sequential patterns by aggregating relevant frequent sequences, this knowledge is composed of multiple constituent components (the left hand side) from multiple data sources which are represented by different feature spaces, or identified by diverse modeling methods. In some cases, this knowledge is also associated with certain impact (influence, action or conclusion, on the right hand side). This paper presents a high-level picture of combined mining and the combined patterns from the perspective of object and pattern relation analysis. Several fundamental aspects of combined pattern mining are discussed, including feature interaction, pattern interaction, pattern dynamics, pattern impact, pattern relation, pattern structure, pattern paradigm, pattern forma-tion criteria, and pattern presentation (in terms of pattern ontology and pattern dy-namic charts). We also briefly illustrate the concepts and discuss how they can be applied to mining complex data for complex knowledge in either a multi-feature, multi-source, or multi-method scenario. 1
Mining Actionable Knowledge for Domain-Driven and Customer-Centric Decision Support
"... Abstract – It has been increasingly critical for businesses to become more customer-centric and more responsive for customer needs. The “voice of customer ” (VOC in short) is a general term to describe the stated and unstated customer needs that can be captured through various customer touchpoints: ..."
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Abstract – It has been increasingly critical for businesses to become more customer-centric and more responsive for customer needs. The “voice of customer ” (VOC in short) is a general term to describe the stated and unstated customer needs that can be captured through various customer touchpoints: sales meetings, surveys, interviews, focus groups, customer support call-center, social media sites, etc.. Most existing approaches in analyzing VOC focus on mining patterns of significant technical interestingness, which are mainly concerned with the data mining method used, but not necessarily meaningful to business problems. To discover knowledge from VOC dataset that can be used for taking actions to business advantages, we develop a hybrid framework that tightly integrates domain knowledge and decision making principles with data-driven approaches. The proposed framework provides following actionable insights: (1) uncover the main themes and the evolving trends of customer needs; (2) identify the gaps between ever-changing customer needs and the service/product provider strategy and development organization structure; (3) effectively prioritize both customer needs and service/product offerings simultaneously by taking into account of customer demography and purchase history, via a novel Semantic Enhanced Link-based Ranking (SELRank) algorithm as described in this paper. This analytical framework that inherently embodies domain specific knowledge has been successfully applied on Xerox Office Group semi-structured VOC dataset to support customer-centric business decisions in both short-term and long-term service/product planning, and adaptive transformation of development organizations.
article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination
- Optical-CDMA in InP,” IEEE This
, 2007
"... Abstract—The importance of social security and social welfare business has been increasingly recognized in more and more coun-tries. It impinges on a large proportion of the population and affects government service policies and people’s life quality. Typical wel-fare countries, such as Australia an ..."
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Abstract—The importance of social security and social welfare business has been increasingly recognized in more and more coun-tries. It impinges on a large proportion of the population and affects government service policies and people’s life quality. Typical wel-fare countries, such as Australia and Canada, have accumulated a huge amount of social security and social welfare data. Emerging business issues such as fraudulent outlays, and customer service and performance improvements challenge existing policies, as well as techniques and systems including data matching and business intelligence reporting systems. The need for a deep understand-ing of customers and customer–government interactions through advanced data analytics has been increasingly recognized by the community at large. So far, however, no substantial work on the mining of social security and social welfare data has been reported. For the first time in data mining and machine learning, and to the best of our knowledge, this paper draws a comprehensive overall picture and summarizes the corresponding techniques and illustra-tions to analyze social security/welfare data, namely, social security data mining (SSDM), based on a thorough review of a large number of related references from the past half century. In particular, we introduce an SSDM framework, including business and research issues, social security/welfare services and data, as well as chal-lenges, goals, and tasks in mining social security/welfare data. A summary of SSDM case studies is also presented with substan-tial citations that direct readers to more specific techniques and practices about SSDM. Index Terms—Data mining, government data mining, public sec-tor, public service, social security data mining (SSDM), social se-curity, social welfare, social welfare data mining. I.
A New Approach for Resolving Conflicts in Actionable Behavioral Rules
"... Knowledge is considered actionable if users can take direct actions based on such knowledge to their advantage. Among the most important and distinctive actionable knowledge are actionable behavioral rules that can directly and explicitly suggest specific actions to take to influence (restrain or e ..."
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Knowledge is considered actionable if users can take direct actions based on such knowledge to their advantage. Among the most important and distinctive actionable knowledge are actionable behavioral rules that can directly and explicitly suggest specific actions to take to influence (restrain or encourage) the behavior in the users' best interest. However, in mining such rules, it often occurs that different rules may suggest the same actions with different expected utilities, which we call conflicting rules. To resolve the conflicts, a previous valid method was proposed. However, inconsistency of the measure for rule evaluating may hinder its performance. To overcome this problem, we develop a new method that utilizes rule ranking procedure as the basis for selecting the rule with the highest utility prediction accuracy. More specifically, we propose an integrative measure, which combines the measures of the support and antecedent length, to evaluate the utility prediction accuracies of conflicting rules. We also introduce a tunable weight parameter to allow the flexibility of integration. We conduct several experiments to test our proposed approach and evaluate the sensitivity of the weight parameter. Empirical results indicate that our approach outperforms those from previous research.
Multi Agent Approach for Evolving Data Mining in Parallel and Distributed Systems using Genetic Algorithms and Semantic Ontology
"... Abstract: Mining information from parallel and distributed systems has been discussed in wide spectrum, and the earlier approaches suffer with the result produced for any query with missing values. Also the approaches have more time complexity which reduces the throughput of the overall systems. We ..."
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Abstract: Mining information from parallel and distributed systems has been discussed in wide spectrum, and the earlier approaches suffer with the result produced for any query with missing values. Also the approaches have more time complexity which reduces the throughput of the overall systems. We propose a new multi agent based approach for mining information from large distributed systems, where the data scattered throughout the network in different systems. Unlike earlier approach the proposed method generates number of agents according to the availability of data and generates results based on the fitness function designed at genetic algorithm. The proposed approach maintains an agent container at each of the location and each has Meta data about the data locations and information. The meta data are stored in form of semantic ontology in order to reduce the data look up time and reduce the time complexity. Upon query submission, the proposed method identifies set of locations where the information is available and generates number of agents to fetch the information from different nodes of the network. The retrieved results are evaluated with genetic algorithm for relevancy of result toward the query submitted. The proposed approach produces efficient results in accuracy of results and time complexity.
S-ANFIS: Sentiment aware adaptive network-based fuzzy inference system for Predicting Sales Performance using Blogs/Reviews
, 2012
"... Abstract: An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, predicting the future quantity for the next period most likely appears to be crucial. This wo ..."
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Abstract: An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, predicting the future quantity for the next period most likely appears to be crucial. This work presents a comparative forecasting methodology regarding to uncertain customer likings in a movie domain via regressive and neuro fuzzy techniques. The main objective is to propose a new future predicting mechanism which is modeled by artificial intelligence approaches including the comparison of both auto regressive method and adaptive network-based fuzzy inference system (ANFIS) techniques to manage the fuzzy demand with incomplete information. The effectiveness of the proposed approach to the demand forecasting issue will be demonstrated using real-world data from a different movie related websites. Here we are going to extract the information from web and utilizing it for the purpose of sales prediction for movies. There are many sales prediction methods but the use of history data will be most efficient way to predict the quality future. Key words: ANFIS, regressive model
P.T.Kavitha et al. / International Journal on Computer Science and Engineering (IJCSE) Performance Evaluation of Algorithms using a Distributed Data Mining Frame Work based on Association Rule Mining
"... Abstract — Numerous current data mining tasks can be implemented effectively only in a distributed data mining. Thus distributed data mining has achieved significant importance in the last decade. The proposed distributed data mining application framework, is a data mining tool. This framework aims ..."
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Abstract — Numerous current data mining tasks can be implemented effectively only in a distributed data mining. Thus distributed data mining has achieved significant importance in the last decade. The proposed distributed data mining application framework, is a data mining tool. This framework aims at developing an efficient association rule mining tool to support effective decision making. Association Rule mining focuses on finding interesting patterns from huge amount of data available in the data warehouses. In order to build strong association rules, it depends on the extraction of association rules by Apriori algorithm, AprioriTID algorithm, AprioriHyprid algorithm, FP growth etc. The efficiency of the distributed data mining framework is determined based on the selection of the algorithm. The object oriented implementation has enabled the system to be platform independent. The use of self defined database format gives an upper hand for the system by operating efficiently without any need for third party database drivers. The mined results can be compared and graphically projected. Finally, some expectations for future work are presented where various modes of graphical representations can be included.
unknown title
"... Abstract: Customer Relationship Management (CRM for short) System emerged in the last decade to reflect the central role of the customer for the strategic positioning of a company. One of the most significant changes in the practice of marketing during the last decade is the shift in emphasis from a ..."
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Abstract: Customer Relationship Management (CRM for short) System emerged in the last decade to reflect the central role of the customer for the strategic positioning of a company. One of the most significant changes in the practice of marketing during the last decade is the shift in emphasis from a transaction orientation customer to the CRM. Now a day’s it is an important edge but now a necessary tool for survival. CRM competence is very important source for enterprises to build and sustain competitive advantage. With the extensive applications in CRM enterprises have plenty of customer data. Main view of CRM is customer understanding, which is properly done will helps to understand customers and thus increases customer life time value. Effectively build CRM will maintain good relationships with customers. Companies have invested or are planning to invest huge amounts to implement CRM strategies, tools and infrastructures inorder to attract and retain profitable customers in today’s increasingly competitive markets. This paper introduces the architecture of CRM based on Domain Driven Data Mining (D 3 M for short) and with advanced technologies for knowing winning strategies. It also discusses the important steps of designing the data warehouse and describes the meaning of D 3 M applied to the CRM and finally evolving of D 3 M to individual service are presented.
