Case Teknos Group Oy Paint Store Transaction Data

Companies operating in challenging business environments, characterized by the proliferation of disruptive technologies and intensifying competition, are obliged to re-evaluate their strategic approach. This has become the norm in the retail industry and traditional brick-and-mortar stores. Particularly local market players with scarce resources are looking into alternative solutions to delivering a unique customer experience with the intention to preserve their profitability. Customer experience has been an integral topic within academic research for decades, and has also substantiated its value in pragmatic contexts. Recent developments in this field have triggered the constitution of customer experience management functions, which aim to adopt a holistic approach to the customer experience. This enforces a quantitative perspective highlighting the role of customer transaction data. Association analysis is one of the most well-known methodology used to detect underlying patterns hidden in large transaction data sets. It uses machine learning techniques to firstly identify frequently purchased product combinations and secondly, to discover concealed associations among the products. The association rules derived and evaluated during the process can potentially reveal implicit, yet interesting customer insight, which may translate into actionable implications. The practical consequences in the framework of this study are referred to as sales increasing strategies, namely targeted marketing, cross-selling and space management. This thesis uses Python programming language in Anaconda’s Jupyter Notebook environment to perform association analysis on customer transaction data provided by the case company. The Apriori algorithm is applied to constitute the frequent itemsets and generate association rules between these itemsets. The interestingness and actionability of the rules will be evaluated based on various scoring measures computed for each rule. The outcomes of this study contribute to finding interesting customer insight and actionable recommendations for the case company to support their success in demanding market conditions. Furthermore, this research describes and discusses the relative success factors from the theoretical point of view and demonstrates the process of association rule mining when applied to customer transaction data

An Empirical Analysis of Mobile Phone Users for Competitive Business Intelligence

With the current globalization drive, most firms rely on Competitive Intelligence to help position them strategically through effective decision-making based on Customer Relationship Management {CRM}, marketing activities and competitors' vulnerability. It is of interest therefore to make decisions based on accurate inferences. Association rules have been widely used in data mining to find patterns in data that reveal combinations that occur at the same time which are called rules. The rules are sometimes too numerous to be used in decision making, hence, the interestingness of the rules are used to select the subset to act upon. This paper aims at evaluating the interestingness of rules gotten from applying association rule mining algorithm to data received from questionnaires of mobiles phone users in Nigeria. A patterr. is interesting if it is easily understood by humans, potentially useful and novel. The evaluation of the rule is done objectively using statistical independence and correlation analysis. This research has helped to reduce the uncertainty and inaccuracy of rules f~om which decisions are based towards the competitive advantage of an organization. Findings from the research revealed the areas of strength and weakness of mobile phone manufacturers and this understanding is used to provide competitive business decisions. which will in turn contribute to the profit of the organizatio

Semantics-based classification of rule interestingness measures

Assessing rules with interestingness measures is the cornerstone of successful applications of association rule discovery. However, as numerous measures may be found in the literature, choosing the measures to be applied for a given application is a difficult task. In this chapter, the authors present a novel and useful classification of interestingness measures according to three criteria: the subject, the scope, and the nature of the measure. These criteria seem essential to grasp the meaning of the measures, and therefore to help the user to choose the ones (s)he wants to apply. Moreover, the classification allows one to compare the rules to closely related concepts such as similarities, implications, and equivalences. Finally, the classification shows that some interesting combinations of the criteria are not satisfied by any index

Text mining with exploitation of user\u27s background knowledge : discovering novel association rules from text

The goal of text mining is to find interesting and non-trivial patterns or knowledge from unstructured documents. Both objective and subjective measures have been proposed in the literature to evaluate the interestingness of discovered patterns. However, objective measures alone are insufficient because such measures do not consider knowledge and interests of the users. Subjective measures require explicit input of user expectations which is difficult or even impossible to obtain in text mining environments. This study proposes a user-oriented text-mining framework and applies it to the problem of discovering novel association rules from documents. The developed system, uMining, consists of two major components: a background knowledge developer and a novel association rules miner. The background knowledge developer learns a user\u27s background knowledge by extracting keywords from documents already known to the user (background documents) and developing a concept hierarchy to organize popular keywords. The novel association rule miner discovers association rules among noun phrases extracted from relevant documents (target documents) and compares the rules with the background knowledge to predict the rule novelty to the particular user (useroriented novelty). The user-oriented novelty measure is defined as the semantic distance between the antecedent and the consequent of a rule in the background knowledge. It consists of two components: occurrence distance and connection distance. The former considers the co-occurrences of two keywords in the background documents: the more the shorter the distance. The latter considers the common connections of with others in the concept hierarchy. It is defined as the length of the connecting the two keywords in the concept hierarchy: the longer the path, distance. The user-oriented novelty measure is evaluated from two perspectives: novelty prediction accuracy and usefulness indication power. The results show that the useroriented novelty measure outperforms the WordNet novelty measure and the compared objective measures in term of predicting novel rules and identifying useful rules

Association Pattern Discovery of Import Export Items in Ethiopia

This paper examines the application of data mining to detect association pattern of customs administration data with market price and currency rate exchange in Ethiopia. The association rule method of data mining is used in this paper to generate the interesting pattern from the data. This study was done to identify the relationships between attributes of custom data and market price to clearly understand the nature of import-export items in Ethiopia. The results of the experiments carried out using association rules revealed that the technique of data mining is applicable to generate knowledge from import and export items in custom administration. Algorithms such as Apriori, Tertius, PredictiveApriori and FliteredApriori were used to generate the associations. One of the resulting associations indicates that there is a strong link between market price and textiles imported. The implication of this research finding is that it clearly identified the association of import-export items with the market price and the effects of those items on the market price and currency rate in Ethiopia

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

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Ph.D.) -- Bilkent University, 2009.Includes bibliographical references leaves 87-94.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 previous 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 arrives, the association rule learning algorithm is run on the last set of transactions, resulting in a new set of 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 the other and also over time for a given expert. In this thesis, we propose 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, including the rule’s content itself, 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 supermarket dataset. The results show that the model can successfully select the interesting ones.Aydın, TolgaPh.D

A holistic approach to information mining : internship experience at Oticon A/S

Internship report presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Knowledge Management and Business IntelligenceIn our days, companies have the huge possibility to understand their customers like never before. They have access to numerous sources of customer related data. Insights that can’t be retrieved from secondary sources, can be gathered through market research. Data is the key element to create customer centric value through data-driven decisions. Making the right decisions based on empirical analysis can become a competitive advantage and a critical success factor for big industry players like Oticon A/S - one of the largest in the world manufacturer of hearing aid devices. This internship report will describe the author’s ten months internship experience in the Market Intelligence Team of Oticon. The description of the projects carried throughout the internship and especially their diversity in terms of tools and methodologies, aims to represent a holistic approach to data mining, showing how the latter should be performed considering it as a part of a larger ecosystem of actors and processes. Quantitative research, development of reporting solutions, database management, and application of frequent pattern mining algorithms, are all used to transform data into actionable knowledge