Expanding the Knowledge Base for More Effective Data Mining

Traditionally, data mining, as part of the knowledge discovery process, relies solely on the information contained in the database to generate patterns. Recently, there has been some recognition in the field that expanding the knowledge passed to the pattern generation phase by including other domain knowledge, may have beneficial effects of the interestingness and actionability of the resulting patterns. In this paper, we present a new knowledge discovery method that uses additional decision rules and the analytic hierarchy process (AHP) to conceptualize and structure the domain, thus capturing a broader notion of domain knowledge upon which data mining can be applied. Based on design science guidelines, we design, develop and implement our method within the domain of a brain trauma intensive care unit

Data Driven Data Mining to Domain Driven Data Mining

In the preceding decade data mining has came into sight as one of the largely energetic areas in information technology Traditional data mining is seriously dependent on data itself and relies on data oriented methodologies So there is a universal necessity in bridging the space among academia and trade is to provide all-purpose domain-related matters in surrounding real-life applications Domain-Driven Data Mining try to build up general principles methodologies and techniques for modelling and reconciling wide-ranging domain-related factors and synthesized ubiquitous intelligence adjacent problem domains with the data mining course of action and discovering knowledge to hold up business decision-makin

Modeling interestingness of streaming association rules as a benefit-maximizing classification problem

Cataloged from PDF version of article.In a typical application of association rule learning from market basket data, a set of transactions for a fixed period of time is used as input to rule learning algorithms. For example, the well-known Apriori algorithm can be applied to learn a set of association rules from such a transaction set. However, learning association rules from a set of transactions is not a one time only process. For example, a market manager may perform the association rule learning process once every month over the set of transactions collected through the last month. For this reason, we will consider the problem where transaction sets are input to the system as a stream of packages. The sets of transactions may come in varying sizes and in varying periods. Once a set of transactions arrive, the association rule learning algorithm is executed on the last set of transactions, resulting in new association rules. Therefore, the set of association rules learned will accumulate and increase in number over time, making the mining of interesting ones out of this enlarging set of association rules impractical for human experts. We refer to this sequence of rules as "association rule set stream" or "streaming association rules" and the main motivation behind this research is to develop a technique to overcome the interesting rule selection problem. A successful association rule mining system should select and present only the interesting rules to the domain experts. However, definition of interestingness of association rules on a given domain usually differs from one expert to another and also over time for a given expert. This paper proposes a post-processing method to learn a subjective model for the interestingness concept description of the streaming association rules. The uniqueness of the proposed method is its ability to formulate the interestingness issue of association rules as a benefit-maximizing classification problem and obtain a different interestingness model for each user. In this new classification scheme, the determining features are the selective objective interestingness factors related to the interestingness of the association rules, and the target feature is the interestingness label of those rules. The proposed method works incrementally and employs user interactivity at a certain level. It is evaluated on a real market dataset. The results show that the model can successfully select the interesting ones. (C) 2008 Elsevier B.V. All rights reserved

Data Mining as Support to Knowledge Management in Marketing

Background: Previous research has shown success of data mining methods in marketing. However, their integration in a knowledge management system is still not investigated enough. Objectives: The purpose of this paper is to suggest an integration of two data mining techniques: neural networks and association rules in marketing modeling that could serve as an input to knowledge management and produce better marketing decisions. Methods/Approach: Association rules and artificial neural networks are combined in a data mining component to discover patterns and customers\u27 profiles in frequent item purchases. The results of data mining are used in a web-based knowledge management component to trigger ideas for new marketing strategies. The model is tested by an experimental research. Results: The results show that the suggested model could be efficiently used to recognize patterns in shopping behaviour and generate new marketing strategies. Conclusions: The scientific contribution lies in proposing an integrative data mining approach that could present support to knowledge management. The research could be useful to marketing and retail managers in improving the process of their decision making, as well as to researchers in the area of marketing modelling. Future studies should include more samples and other data mining techniques in order to test the model generalization ability

From Classification Rules to Action Recommendations

Rule induction has attracted a great deal of attention in Machine Learning and Data Mining. However, generating rules is not an end in itself because their applicability is not straightforward especially when the number of rules is large. Ideally, the user would ultimately like to use these rules to decide which actions to take. In the literature, this notion is usually referred to as actionability. The contribution of this paper1 is two-fold: first we propose a survey of the main approaches developed to address actionability. This topic has received growing attention in the past years. We present a classification of the main research in this area as well as a comparative study between the different approaches. Second, we propose a new framework to address actionability. Our goal is to lighten the burden of analyzing a large set of classification rules when the user is confronted with an "unsatisfactory situation" and needs help to decide what appropriate actions to take in order to remedy the situation. The method consists in comparing the situation to a set of classification rules. This is achieved by using a suitable distance that allows one to suggest action recommendations requiring minimal changes to improve the situation. We propose the algorithm DAKAR for learning action recommendations and we present an application to environment protection. Our experiment shows the usefulness of our contribution for action recommendation but also raises some concerns about the impact of the redundancy of a set of rules in learning action recommendations of good quality

CAPRE: A New Methodology for Product Recommendation Based on Customer Actionability and Profitability

International audienceRecommender systems can apply knowledge discovery techniques to the problem of making product recommendations. This aims to establish a customer loyalty strategy and thus to optimize the customer life time value. In this paper we propose CAPRE, a data-mining based methodology for recommender systems based on the analysis of turnover for customers of specific products. Contrary to classical recommender systems, CAPRE does not aspire to predict a customer's behavior but to influence that behavior. By measuring the actionability and profitability of customers, we have the ability to focus on customers that can afford to spend larger sums of money in the target business. CAPRE aggregates rules to extract characteristic purchasing behaviors, and then analyzes the counter-examples to detect the most actionable and profitable customers. We measure the effectiveness of CAPRE by performing a cross-validation on the MovieLens benchmark. The methodology is applied to over 10,000 individual customers and 100,000 products for the customer relationship management of VM Matériaux company, thus assisting the salespersons' objective to increase the customer value

Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking

Machine-learned models are often described as "black boxes". In many real-world applications however, models may have to sacrifice predictive power in favour of human-interpretability. When this is the case, feature engineering becomes a crucial task, which requires significant and time-consuming human effort. Whilst some features are inherently static, representing properties that cannot be influenced (e.g., the age of an individual), others capture characteristics that could be adjusted (e.g., the daily amount of carbohydrates taken). Nonetheless, once a model is learned from the data, each prediction it makes on new instances is irreversible - assuming every instance to be a static point located in the chosen feature space. There are many circumstances however where it is important to understand (i) why a model outputs a certain prediction on a given instance, (ii) which adjustable features of that instance should be modified, and finally (iii) how to alter such a prediction when the mutated instance is input back to the model. In this paper, we present a technique that exploits the internals of a tree-based ensemble classifier to offer recommendations for transforming true negative instances into positively predicted ones. We demonstrate the validity of our approach using an online advertising application. First, we design a Random Forest classifier that effectively separates between two types of ads: low (negative) and high (positive) quality ads (instances). Then, we introduce an algorithm that provides recommendations that aim to transform a low quality ad (negative instance) into a high quality one (positive instance). Finally, we evaluate our approach on a subset of the active inventory of a large ad network, Yahoo Gemini.Comment: 10 pages, KDD 201

Marketing Insight: The Construct, Antecedents, Implications, and Empirical Testing

While firms’ data are exponentially growing, the level of marketing insight within firms is not. Insight is becoming a buzzword and dissipating its value due to the lack of conceptual understanding. This research develops and tests a marketing insight nomological network to answer how firms can generate marketing insights and what are the consequences of managing marketing insights. The research findings are relevant for the literature because (1) define the term theoretical domain, (2) lead companies to increase their chances to generate marketing insights and (3) establish the activities to improve the positive financial effect of marketing insight generation