The performance of Case-Based Reasoning (CBR) systems is highly depend on the performance of the retrieval phase. Usually, if the case memory has a large number of cases the system turn to be very slow. Several mechanisms have been proposed in order to prevent a full search of the case memory during the retrieval phase. In this work we propose a clustering technique applied to the memory of cases. But this strategy is applied to an intermediate level of information that defines paths to the cases. Algorithms to the retrieval and retention phase are also presented.- (undefined

Amaral, Luis

Costa, Ernesto

Tome, Paulo

English

Universidade do Minho: RepositoriUM

IMPROVING CASE RETRIEVAL PERFORMANCE THROUGH THEUSE OF CLUSTERING TECHNIQUESPaulo ToméDepartment of Computing, Polytechnic of Viseu, Viseu, PortugalErnesto CostaDepartment of Computing, University of Coimbra, Coimbra, PortugalLuı́s AmaralDepartment of Information Systems, University of Minho, Guimarães, PortugalKeywords: Case-Based Reasoning, Clustering Techniques, Case-Base Maintenance.Abstract: The performance of Case-Based Reasoning (CBR) systems is highly depend on the performance of the re-trieval phase. Usually, if the case memory has a large number of cases the system turn to be very slow. Severalmechanisms have been proposed in order to prevent a full search of the case memory during the retrievalphase. In this work we propose a clustering technique applied to the memory of cases. But this strategy isapplied to an intermediate level of information that defines paths to the cases. Algorithms to the retrieval andretention phase are also presented.1 INTRODUCTIONCBR is a method (Watson, 1999) that allows to solveproblems based in previous resolved ones (Kolodner,1993; Mantaras et al., 2006). Every CBR systemcomprises a retrieval phase, a re-using phase and aretention phase (Aamodt and Plaza, 1994). Thesephases have different impacts on the performanceof the CBR. According with Smyth and McKenna(Smyth and McKenna, 1999), the performance of aCBR system can be measured according to three cri-teria:• Effiency - the average problem solvingtime;• Competence - the range of target problemsthat can be successfully solved;• Quality - the average quality of a proposedsolution.As mentioned by Smyth and McKenna (Smythand McKenna, 1999), the retrieval process deservedalways highest interest of the CBR research commu-nity because it has an high influence on the CBR sys-tem performance.The retrieval process involves the combination oftwo procedures: similarity evaluation and searchingin the memory case. The first procedure judges thesimilarity of the current problem with the ones previ-ously resolved. If the case memory have a large num-ber of cases, the number of similarity evaluations islarge representing a computational burden.Some authors tried to improve the retrieval phaseperformance using case memory structures. The aimof these structures is to organize the case memory inway that enable a fast case access avoiding the sim-ilarity evaluation of all cases in memory. There areseveral examples of case memory structures propos-als, for example, Kolodner (Kolodner, 1993) identi-fies four ways of organizing the case memory. Fol-lowing those proposals Schaaf (Schaaf, 1996) orga-nizes cases in a network by considering cases as apolyhedron with a face for each aspect. And Wolver-ton (Wolverton, 1994) organizes the case memory ina semantic network. Within that semantic network,small subgraphs of nodes and links which representaggregate concepts are explicitly grouped togetheras conceptual graphs. More recently Yang and Wu(Yang and Wu, 2000) split the case into a set of clus-450Tomé P., Costa E. and Amaral L. (2008).IMPROVING CASE RETRIEVAL PERFORMANCE THROUGH THE USE OF CLUSTERING TECHNIQUES.In Proceedings of the Tenth International Conference on Enterprise Information Systems - AIDSS, pages 450-454DOI: 10.5220/0001687704500454Copyright c© SciTePressters distributed by different sites/machines.Furthermore, the structure of the case mem-ory are directly related with the case base mainte-nance (CBM) methods (Wilson and Leake, 2001).The objective of the CBM is maintaining consis-tency, preserving competence and controlling case-base growth.The previous presented approaches does not ad-dress the problem of missing case features. This prob-lem is currently addressed in CBR research field (GUand Aamodt, 2005; Gu and Aamodt, 2006).We propose the use of clustering techniques to im-prove the performance of the CBR during the retrievalphase. However, we use different approach of Yangand Wu (Yang and Wu, 2000). We do not split thecase memory into distinct locations instead we useclusters of links to cases. Besides that, our proposalalso deals with cases with missing features. In Sec-tion 2 we review some clustering concepts essentialto the understanding of our proposal shown in section3. In section 3 we present also the results of the ap-plication of our proposal to a CBR system with 915cases.2 THE CLUSTERINGTECHNIQUEClustering techniques organizes data into groups thatare meaningful, useful or both (Tan et al., 2006). Onegroup of data is called a cluster, while the entire col-lection of clusters is commonly referred to as a clus-tering.Two types of clustering can be considered: par-tional and hierarchical. A clustering is hierarchical ifwe permit clusters to have subclusters. In partionalclustering the data is divided into non-overlappingclusters. Tan et al. (Tan et al., 2006) identified fivetypes of clusters: well-separated, prototype-based,graph-based, density based and shared-based. In ourwork we will consider prototype-based type. A set ofcases is grouped into a cluster with one representativeelement. Then, in the retrieval phase the number ofsimilarity evaluations is reduced considerably.There are several techniques to split the data, butk-means and k-medoid are two of the most prominenttechniques associated to prototype-based techniques(Tan et al., 2006). K-means defines a prototype interms of a centroid, while k-medoid defines defines aprototype in terms of a medoid. The medoid is oneelement of the cluster while the centroid is the meanof the cluster. In our work we use the k-medoid tech-nique. So each cluster is represented by the most rep-resentative case among all cases in the group. Thereare also different proposals to measure similarity be-tween data: the Euclidean and cosine distance are themost used similarity measures. The similarity mea-sure is used whenever a new case has to be added tothe case memory. Naturally the updated cluster needto update its prototype.3 THE APPLICATION OFCLUSTERING TECHNIQUESTO THE CBR RETRIEVALPHASEOur proposal, shown in figure 1, has two levels of in-formation. The first level is formed by a set of linksto the case memory and the second level is the casememory database. The case links are paths to casesmemory. And the clustering technique is applied tocase links information. The first level of informationrequires a low amount of storage space however de-creases the waiting time of the retrieval process. Wedo not considered the division of the database casememory because it is useful to access a case fromdifferent ways. The figure 1 illustrates the storagescheme and we can see that groups of clusters are theinterface between CBR process and the database ofcases.Group of clustersCase Links1Case MemoryGroup of clustersCase Links2Group of clustersCase LinksnRetrieval ProcessReusingRevisionProcess 1…Process 2Process nRetention...Figure 1: CBR system structure.Each group has clusters of links to cases. Andeach cluster, as shown in table 1, has a reference tothe medoid of the cluster and links to a set of casesthat constitute the cluster.Each Group of clusters is identified by a binary ar-ray codification. The binary codification scheme fol-lows the proposal of Kolodner, table 2, who definesthat a case is formed by a Problem and by a Solu-tion. And the Problem consists in Objective and a setIMPROVING CASE RETRIEVAL PERFORMANCE THROUGH THE USE OF CLUSTERING TECHNIQUES451Table 1: Cluster definition.Clus=< LMed,SCl> Med= link to case SCl={link to case}Where:Clus - ClusterLMed - Link to medoidSCl - Set of Cluster link;C - CharacteristicTable 2: Case Structure.Case=< P,S>P =< O,Cs>Cs= {C}Where:P - ProblemO - ObjectiveCs - Set of characteristics;C - CharacteristicTable 3: Case Example.Cas1 =< P,S>P =< O1,Cs>Cs=< C1,C2,C3 >of Characteristics. Table 3 shows a case with threeCharacteristics. So each position of the binary arrayis associated to a particular feature of a case, where1 (0) indicates the availability (non-availibility) of thefeature.The first positions on the right side of the array areused to represent objectives. The remaining positionsare used to represent characteristics. For example, us-ing sixteen bits with the division illustrated in table 4,the caseCas1, shown in table 2, addresses the groupwith the following binary array 0000000001110001.Table 4: Addressing Group Cluster.C12C11C10 C9 C8 C7 C6 C5 C4 C3 C2 C1 O4 O3 O2 O1However to deal with missing characteristics thecases belong to more than one group. All combinationof the available characteristics and objective definedifferent groups. In table 5 it is presented the com-binations of characteristics and objective for the caseCas1. The caseCas1 is associated to seven groups(figure 2), in each group clustering might be achievedwith a distinct number of clusters.The Retrieval process was modified to enable theadoption of the principles previously described. Table6 shows the steps of the algorithm when a new prob-lem is presented: 1)- identification of the clusteringTable 5: Combinations example toCas1 case.C12 C11 C10 C9 C8 C7 C6 C5 C4 C3 C2 C1 O4 O3 O2 O1 Id0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 Co10 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 Co20 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 Co30 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 Co40 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 Co50 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 Co60 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 Co7Co1ClusterA1ClusterA2ClusterAn...ClusterB1ClusterB2ClusterBn...ClusterC1ClusterC2ClusterCn...ClusterD1ClusterD2ClusterDn...ClusterE1ClusterE2ClusterEn...ClusterF1ClusterF2ClusterFn...ClusterG1ClusterG2ClusterGn...Co2 Co3 Co4 Co5 Co6 Co7Groups of clustersFigure 2: Example of a database of group of case links.group 2)-identification of the cluster within the groupusing a similarity measure; 3)- finally the case is com-pared with all cases in the cluster. In the second stepthe Default Difference measure strategy (Bogaertsand Leake, 2004) is applied only to the medoid of theclusters. In the third step, different similarity evalua-tions are used.The retention process algorithm, shown in table 7,was also redefined. This process was parallelized, e.g.the retention in each Group of cluster is implementedby different program processes. The process beginswith the determination of all possible combinationsbetween the available characteristics and the objectiveof the case. Then for each combination, the case is in-serted in respective Group of clusters. This insertionprocess is parallelized. Each insertion in a Group de-termines: 1)-evaluation of the similarity with the clus-ter medoids 2)- identification of the cluster to insertthe case, 3) actualization of the medoid of the clusterwhere the case was assigned. The similarity measurestrategy used in step two is alsoDefault Difference.In a group a new cluster is created whenever a binarysimilarity evaluation results in a zero.The medoid is computed using the use of the ex-pressionpos med=n f eatures∑i=1pos(value o f f eature(i))∗weight( f eature(i)) (1)where pos(valueo f f eature(i)) is the posi-ICEIS 2008 - International Conference on Enterprise Information Systems452Table 6: Retrieval Algorithm./* —————————————————————-Cas is the case for which it is search a solutionProp Cas is the proposed case—————————————————————-*/procedure retrieval(Cas in Case, Propcas out Case)Clusgroup ClusterGroup;Clus Cluster;Sim Similarity;Sim a Similarity;beginClusgroup← Determineclustergroup(Cas.Obj,Cas.Cars);Clus← 0;Sim← 0;For each Cluster in Clusgroup dobeginSim a← Similarity(Cas, Cluster(i).medoid);if Sim a> Sim thenbeginSim← Sim a;Clus← Cluster(i);end;end;Sim← 0;For each Case in Clus dobeginSim a← Similarity(Cas, case(i));if Sim a> Sim thenbeginSim← Sim a;Propcas← Case(i);end;end;end;0,002,004,006,008,0010,0012,0014,0016,0018,0020,0022,0024,0026,0028,0030,0032,0034,0036,0038,0040,0042,0044,0046,0048,0050,0052,0054,0056,0058,0060,0062,0064,0066,0068,0070,001 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361 381 401 421 441 461 481 501 521 541 561 581 601 621 641 661 681 701 721 741 761 781 801 821 841 861 881 901Number of casesRecalltimeFigure 3: Waiting time before clustering technics applica-tion.tion of feature in a ordered set of values andweight( f eature(i)) is the weight of thef eature(i).We compare the described strategy with a flatmemory case for a CBR system with 915 cases. Aswe can see in figure 3, the system had bad perfor-mance after the insertion of three hundred cases in thecase memory.After the application of the clustering approachTable 7: Retention Algorithm./* ———————————————-Cas is the case that will be retained———————————————–*/procedure retention(Cas in Case)Combs Combinations;Combination TCombination;Clusgroup ClusterGroup;Clus Cluster;Sim Similarity;Sim a Similarity;beginCombs← Generateall combinations(Cas.Obj,Cas.Cars);For each Combination in Combs dobeginClusgroup← Determineclus group(Combination(i));Sim← 0;For each Cluster in Clusgroup dobeginSim a← Similarity(Cas, Cluster(j).medoid);if Sim a> Sim thenbeginSim← Sim a;Clus← Clus(i);end;insert casecluster(Clus,Cas);recalculatemedoid(Cluster(i));end;end;05101520251 26 51 76 101 126 151 176 201 226 251 276 301 326 351 376 401 426 451 476 501 526 551 576 601 626 651 676 701 726 751 776 801 826 851 876 901Figure 4: Waiting time after clustering technics application.the waiting time for each of the 915 cases was lessthan 1 second. Although, the retention process takesmore time than the flat memory case. The figure 4shows the retention waiting time for all of the 915cases. Although, the total waiting time is smaller afterthe application of the clustering techniques as shownin figure 4.4 CONCLUSIONSThis paper presents an approach that can be used toimprove the performance of CBR system Retrievalphase. The approach uses clustering techniques toIMPROVING CASE RETRIEVAL PERFORMANCE THROUGH THE USE OF CLUSTERING TECHNIQUES453structure the case memory. The use of clustering tech-niques define a particular case memory structure andconsequently algorithms for the retrieval and reten-tion phase are proposed.The approach was tested in a CBR system with915 cases. The result show that the overall systemperformance is improved. It is important to notice thatthe retrieval waiting time was considerably reducedand the total waiting time (time of retrieval and reten-tion) is also substantially smaller than with a flat casememory organization.REFERENCESAamodt, A. and Plaza, E. (1994). Case-based reasoning:Foundational issues, methodological variations andsystems approaches.AI-Communications, 7(1):39–52.Bogaerts, S. and Leake, D. (2004). Facilitating cbr forincompletely-described cases: Distance metrics forpartial problem descriptions. InECCBR 2004, pages62–74.GU, M. and Aamodt, A. (2005). A knowledge-intensivemethod for conversational cbr. In6th InternationalConference on Case-Based Reasoning, pages 296–311. Springer-Verlag.Gu, M. and Aamodt, A. (2006). Dialog learning in con-versational cbr. In19th International FLAIRS Confer-ence, pages 358–363, Florida, EUA. AAAI Press.Kolodner, J. (1993).Case-Based Reasoning. Morgan Kauf-mann Publishers.Mantaras, R. 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(1994). Retrieving Semantically DistantAnalogies. Ph.d thesis, Stanford University.Yang, Q. and Wu, J. (2000). Keep it simple: A case-basemaintenance policy based on clustering and informa-tion theory. InCanadian AI 2000, pages 102–114.Springer-Verlag.ICEIS 2008 - International Conference on Enterprise Information Systems454

Improving case retrieval performance through the use of clustering techniques

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Improving case retrieval performance through the use of clustering techniques

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