ACTIONABLE KNOWLEDGE DISCOVERY: THE ANALYTIC HIERACHY PROCESS APPROACH Ikuvwerha L.O.; Odumuyiwa, V.T; Ogunbiyi T.D.; Uwadia, C.O.; Abass O. DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF LAGOS AKOKA, LAGOS

INTRODUCTION There is an urgent need for a new generation of computational theories and tools to assist humans in extracting useful information (knowledge) from the rapidly growing volumes of digital data. One of the central problems of data mining is the discovering of interestingness and actionable patterns. “Actionable patterns” is referred to knowledge that end-user (which could be decision –maker) can act upon or take action on. Therefore it is important to filter these patterns through the use of some measures (interestingness) to produce patterns that are actionable that is usable to the end-users

STATEMENT OF PROBLEM The blind application of data mining methods(which is criticize as data dredging in the statistical literature) can easily leading to discovery of meaningless and invalid patterns. This is because Data mining has only concentrate more on the mining techniques.

RELATED WORKS According to Cao & Zhang (2007) “the traditional data-centered mining methodology could be complimented by the involvement of domain-related social intelligence in data mining which leads to domain-driven data mining“. Simply knowing many algorithms used for data analysis is not sufficient for a successful data mining (DM) and Knowledge Discovery (KD) project. Kavitha and Ramaraj (2013), presented a framework that uses combined mining and composite approach to generate actionable patterns in terms of rules. The concept from meta- learning that uses decision theory was used to formulate a utility interestingness measures (objective and subjective). Zoo and Mushroom data from the University of California Irvine was used for the experiment.

RELATED WORK………CON’T Cao (2012), summarised the extreme imbalances that exist in the current data mining, which are: Algorithm imbalance Pattern Imbalance Decision Imbalance The paper treats AKD as closed optimisation problem. AKD := OPTIMAZATION (PROBLEM, DATA, ENVIRONMENT, MODEL, DECISION) AKD is a problem-solving Process that transforms business problem ѱ with problem status t to a problem- solution ф. Ѱ(./t) ф( ). ……..1

RELATED WORK……….CON’T Amruta and Balachandran (2013), reviewed the four most used AKD frameworks for business need. These frameworks are: Postanalysis-interestingness-based AKD Unified-interestingness-based AKD Combined-mining-based AKD Multisource combined- mining- based AKD Their performance (the numbers of actionable pattern sets) was evaluated under decision making system using a real time tennis data set. The multisource combine-mining-based AKD performs better than the others.

What is Data Mining? According to Fayyad, Piatetsky-Shapiro and Smyth (1996), who define it as “the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.” Data mining is a process that takes data as input and outputs knowledge.

KNOWLEDGE DISCOVERY PROCESS It is defined as the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. It consists of many steps (one of them is Data Mining), each attempting to complete a particular discovery task and each accomplished by the application of a discovery method. Knowledge discovery concerns the entire knowledge extraction process.     

WHAT IS ACTIONABLE KNOWLEDGE? The term “actionable pattern” refers to knowledge that can be uncovered in large complex databases and can act as the impetus for some action. It is important to distinguish these actionable patterns from the lower value patterns that can be found in great quantities and with relative ease through so called data dredging.

WHAT IS KNOWLEDGE? Knowledge is a subset of information. But it is a subset that has been extracted, filtered, or formatted in a very special way. More specifically, the information we call knowledge is information that has been subjected to, and passed tests of validation.

The basic differences between KDP and AKD ASPECTS KDP AKD (DOMAIN – DRIVEN) OBJECT MINED Data tells the story Data and Domain tells the story AIM Develop innovative approach Generate business impacts OBJECTIVE Algorithms are the focus Solving business problem is the focus DATA SET Mining abstract and refined data set Mining constraints real- life data PROCESS Data mining is an automated process Humans are integrated into the process EVALUATION Based on technical metrics Based on actionable options GOAL Let data create and verify research innovation. Push novel algorithms to discover knowledge of research interest. Let data and metasynthetic knowledge tell the hidden business story. Discover actionable knowledge to satisfy end user

Measuring Knowledge Actionability Actionability of a pattern: Given a pattern P, its actionable capability act() is described as to what degree it can satisfy both technical interestingness and business one. It is not only interesting to data miners, but generally interesting to decision- makers. ∀ x ∈ I, ∃P : x.tech_int(P) ∧ x.biz_int(P) ∧ x.act(P) Therefore, the work of actionable knowledge discovery must focus on knowledge findings, which can not only satisfy technical interestingness but also business measures.

THE ANALYTIC HIERARCHY PROCESS (AHP) The foundation of the Analytic Hierarchy Process (AHP) is a set of axioms that carefully delimits the scope of the problem environment (Saaty 1996). It is based on the well-defined mathematical structure of consistent matrices and their associated right- eigenvector's ability to generate true or approximate weights, (Saaty,1980). It converts individual preferences into ratio scale weights that can be combined into a linear additive weight w(a) for each alternative a. The resultant w(a) can be used to compare and rank the alternatives and, hence, assist the decision maker in making a choice.

THE PROPOSED CONCEPTUAL MODEL AHP-AKD.

ILLUSTRATIVE EXAMPLE According to Mcgarry (2005), a data mining algorithm produced the following patterns.   Patterns 1: IF (age > 60) ∧ (salary = high) THEN loan =approved Patterns 2: IF (age < 60) ∧ (salary = average) ∧ (Record = poor) THEN loan = not approved Patterns 3: IF (age < 60) ∧ (salary = low) THEN loan = approved While the end-user/expert defined pattern is IF (age > 50) ∧(salary = low ) THEN loan = not approved. The major issue is to find the pattern that is more actionable in terms of less risk.

Structuring the problem using AHP The goal is to find actionable pattern. The criteria used are actionability, unexpected and novel. The alternatives are Pattern 1, pattern 2 and pattern 3.

Table1:PAIRWISE MATRIX RESULTS λmax = 3.041 CR= 0.0356 This result shows that actionable is of more important with 57%, followed by unexpectedness with 32%, and Novel with 11%. In finding actionable patterns or knowledge, actionability of the pattern comes first followed by unexpectedness and novel. FACTORS ACTIONABLE   UNEXPECTED NOVEL NORMALISED EIGEN VECTOR 1 2 5 0.5701 1/2 3 0.3207 1/5 1/3 0.1092

Table 2: Pairwise comparison matrix for the Alternative with respect actionable factor λmax = 3.002 CR= 0.0138 the patterns are evaluated according to their actionability. We find out that pattern 1 is more actionable with 65%, followed by Pattern 2 with 23%, and pattern 3 with 12%. ACTIONABLE   PATTERN 1 PATTERN 2 PATTERN 3 NORMALISED EIGEN VECTOR 1 5 3 0.6485 0.33 0.2296 0.2 0.1219

Table 3:Pairwise comparison matrix for the Alternative with respect actionable factor λmax = 3.0392 CR= 0.033. This result shows that according to pattern unexpectedness, the pattern are ranked as follow: pattern 3 with 64.13%, pattern 2 with 23.755 and Pattern 1 with 12.14%. From this it is clear that pattern 3 contradicts the user’s belief and it is therefore unexpected. This also confirm the result from the Mcgarry (2005) results using unexpectedness as a factor. UNEXPECTED   PATTERN 1 PATTERN 2 PATTERN 3 NORMALISED EIGEN VECTOR 1 0.5 0.2 0.1213 2 0.33 0.2374 5 3 0.6413

Table 4 : Overall priority This result shows that the pattern are ranked as follow: pattern 1 with 46.64%, pattern 2 with 24.07% and Pattern 3 with 29.29%. From this it is clear that pattern 1 is seen to be more actionable followed by pattern 3 and then pattern 2 ALTERNATIVES OVREALL PRIORITY PATTERN 1 0.4664 PATTERN 2 0.2407 PATTERN 3 0.2929

CONCLUSION The major issue in actionable knowledge discovery is the interestingness measure: objective and subjective measure. The proposed conceptual model uses the AHP as the subjective measure This research therefore concludes that AHP can be effectively used as subjective interestingness measure for actionable knowledge.

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