20,056 research outputs found
Robust and cost-effective approach for discovering action rules
The main goal of Knowledge Discovery in
Databases is to find interesting and usable patterns, meaningful
in their domain. Actionable Knowledge Discovery came to
existence as a direct respond to the need of finding more usable
patterns called actionable patterns. Traditional data mining
and algorithms are often confined to deliver frequent patterns
and come short for suggesting how to make these patterns
actionable. In this scenario the users are expected to act.
However, the users are not advised about what to do with
delivered patterns in order to make them usable. In this paper,
we present an automated approach to focus on not only creating
rules but also making the discovered rules actionable.
Up to now few works have been reported in this field which
lacking incomprehensibility to the user, overlooking the cost
and not providing rule generality. Here we attempt to present a
method to resolving these issues. In this paper CEARDM
method is proposed to discover cost-effective action rules from
data. These rules offer some cost-effective changes to
transferring low profitable instances to higher profitable ones.
We also propose an idea for improving in CEARDM method
Actionable knowledge discovery : methodologies and frameworks
University of Technology, Sydney. Faculty of Engineering and Information Technology.Most data mining algorithms and tools stop at the mining and delivery of patterns satisfying expected technical interestingness. There are often many patterns mined but business people either are not interested in them or do not know what follow-up actions to take to support their business decisions. This issue has seriously affected the widespread employment of advanced data mining techniques in greatly promoting enterprise operational quality and productivity.
In this thesis, a formal and systematic view of actionable knowledge discovery (AKD for short) has been proposed from the system and microeconomy perspectives. AKD is a closed-loop optimization problem-solving process from problem definition, framework/model design to actionable pattern discovery, and to deliver operationalizable business rules that can be seamlessly associated or integrated with business processes and systems. To support AKD, corresponding methodologies, frameworks and tools have been proposed with case studies in the real world to address critical challenges facing the traditional KDD and. to cater for crucially important factors surrounding real-life AKD.
First, a comprehensive survey and retrospection on the existing data mining methodologies, issues and challenges in actionable knowledge discovery are reviewed.
Second, a practical data mining methodology: domain driven data mining is addressed.
Third, several frameworks have been proposed to support domain drivenactionable knowledge discovery.
Fourth, case studies of domain-driven actionable pattern mining in stock markets and social security data are presented to demonstrate the usefulness and potential of the proposed domain driven actionable knowledge discovery.
In summary, this thesis explores in detail how domain driven actionable knowledge discovery can be effectively and efficiently applied to the discovery and delivery of knowledge satisfying both technical and business concerns as well as to support smart decision-making in the real world. The issues and techniques addressed in this thesis have potential to promote the research on critical KDD challenges, and contribute to the paradigm shift from data-centered and technical significance-oriented hidden pattern mining to domain-driven and balanced actionable knowledge discovery. The proposed methodologies and frameworks are flexible, general and effective to be expanded and applied to mining real-life complex data for actionable knowledge
RESEARCH ISSUES CONCERNING ALGORITHMS USED FOR OPTIMIZING THE DATA MINING PROCESS
In this paper, we depict some of the most widely used data mining algorithms that have an overwhelming utility and influence in the research community. A data mining algorithm can be regarded as a tool that creates a data mining model. After analyzing a set of data, an algorithm searches for specific trends and patterns, then defines the parameters of the mining model based on the results of this analysis. The above defined parameters play a significant role in identifying and extracting actionable patterns and detailed statistics. The most important algorithms within this research refer to topics like clustering, classification, association analysis, statistical learning, link mining. In the following, after a brief description of each algorithm, we analyze its application potential and research issues concerning the optimization of the data mining process. After the presentation of the data mining algorithms, we will depict the most important data mining algorithms included in Microsoft and Oracle software products, useful suggestions and criteria in choosing the most recommended algorithm for solving a mentioned task, advantages offered by these software products.data mining optimization, data mining algorithms, software solutions
Discovery of Actionable Patterns in Databases: The Action Hierarchy Approach
An approach to defining actionability as a measure of
interestingness of patterns is proposed. This approach
is based on the concept of an action hierarchy which
is defined as a tree of actions with patterns and pattern
templates (data mining queries) assigned to its
nodes. A method for discovering actionable patterns
is presented and various techniques for optimizing the
discovery process are proposed.Information Systems Working Papers Serie
Mining actionable combined patterns satisfied both utility and frequency criteria
University of Technology Sydney. Faculty of Engineering and Information Technology.In recent years, the importance of identifying actionable patterns has become increasingly recognized so that decision-support actions can be inspired by the resultant patterns. A typical shift is on identifying high utility rather than highly frequent patterns. Accordingly, High Utility ltemset (HUI) Mining methods have become quite popular as well as faster and more reliable than before. However, the current research focus has been on improving the efficiency while the coupling relationships between items are ignored. It is important to study item and itemset couplings inbuilt in the data. For example, the utility of one itemset might be lower than a user-specified threshold, whereas the utility may be larger when an additional itemset takes part in; and vice versa, an item's utility might be high until another one joins in. In this way, although some absolutely high utility itemsets can be discovered, it is sometimes easy to find out that many redundant itemsets sharing the same item are mined (e.g., if the utility of a diamond is high enough, all its supersets are proved to be HUIs). Such itemsets are not actionable, as sellers cannot make higher profit if marketing strategies are created on top of such findings. To this end, this thesis introduces a new framework for mining actionable high utility association rules, called Combined Utility-Association Rules (CUAR), which aims to find high utility and strongly associated itemset combinations which include item/itemset relations. The algorithm is proved to be efficient per experimental outcomes on both real and synthetic datasets
FixMiner: Mining Relevant Fix Patterns for Automated Program Repair
Patching is a common activity in software development. It is generally
performed on a source code base to address bugs or add new functionalities. In
this context, given the recurrence of bugs across projects, the associated
similar patches can be leveraged to extract generic fix actions. While the
literature includes various approaches leveraging similarity among patches to
guide program repair, these approaches often do not yield fix patterns that are
tractable and reusable as actionable input to APR systems. In this paper, we
propose a systematic and automated approach to mining relevant and actionable
fix patterns based on an iterative clustering strategy applied to atomic
changes within patches. The goal of FixMiner is thus to infer separate and
reusable fix patterns that can be leveraged in other patch generation systems.
Our technique, FixMiner, leverages Rich Edit Script which is a specialized tree
structure of the edit scripts that captures the AST-level context of the code
changes. FixMiner uses different tree representations of Rich Edit Scripts for
each round of clustering to identify similar changes. These are abstract syntax
trees, edit actions trees, and code context trees. We have evaluated FixMiner
on thousands of software patches collected from open source projects.
Preliminary results show that we are able to mine accurate patterns,
efficiently exploiting change information in Rich Edit Scripts. We further
integrated the mined patterns to an automated program repair prototype,
PARFixMiner, with which we are able to correctly fix 26 bugs of the Defects4J
benchmark. Beyond this quantitative performance, we show that the mined fix
patterns are sufficiently relevant to produce patches with a high probability
of correctness: 81% of PARFixMiner's generated plausible patches are correct.Comment: 31 pages, 11 figure
Actionable insights through association mining of exchange rates: a case study
Association mining is the methodology within data mining that researches associations among the elements of a given set, based on how they appear together in multiple subsets of that set. Extensive literature exists on the development of efficient algorithms for association mining computations, and the
fundamental motivation for this literature is that association mining reveals actionable insights and enables better policies. This motivation is proven valid for domains such as retailing, healthcare and software engineering, where elements of the analyzed set are physical or virtual items that appear in transactions. However, the literature does not prove this motivation for databases where items are “derived items”, rather than actual items. This study investigates the association patterns in changes of exchange rates of US Dollar, Euro and Gold in the Turkish economy, by representing the percentage changes as “derived items” that appear in “derived market baskets”, the day
on which the observations are made. The study is one of the few in literature that applies such a mapping and applies association mining in exchange rate analysis, and the first one that considers the Turkish case. Actionable insights, along with their policy implications, demonstrate the usability of the developed analysis approach
Predictive User Modeling with Actionable Attributes
Different machine learning techniques have been proposed and used for
modeling individual and group user needs, interests and preferences. In the
traditional predictive modeling instances are described by observable
variables, called attributes. The goal is to learn a model for predicting the
target variable for unseen instances. For example, for marketing purposes a
company consider profiling a new user based on her observed web browsing
behavior, referral keywords or other relevant information. In many real world
applications the values of some attributes are not only observable, but can be
actively decided by a decision maker. Furthermore, in some of such applications
the decision maker is interested not only to generate accurate predictions, but
to maximize the probability of the desired outcome. For example, a direct
marketing manager can choose which type of a special offer to send to a client
(actionable attribute), hoping that the right choice will result in a positive
response with a higher probability. We study how to learn to choose the value
of an actionable attribute in order to maximize the probability of a desired
outcome in predictive modeling. We emphasize that not all instances are equally
sensitive to changes in actions. Accurate choice of an action is critical for
those instances, which are on the borderline (e.g. users who do not have a
strong opinion one way or the other). We formulate three supervised learning
approaches for learning to select the value of an actionable attribute at an
instance level. We also introduce a focused training procedure which puts more
emphasis on the situations where varying the action is the most likely to take
the effect. The proof of concept experimental validation on two real-world case
studies in web analytics and e-learning domains highlights the potential of the
proposed approaches
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