A Review of Clustering Algorithm Based On Swarm Intelligence

Transcription

1 A Revew of Clusterng Algorthm Based On Swarm Intellgence RAJNI MISHRA M.Tech, Department of CSE TIT BHOPAL, Inda BHUPESH GAUR Head of Department of CSE TIT BHOPAL, Inda ABSTRACT Clusterng algorthm s powerful tool n data analyss. The process of clusterng done by the selecton of smlar features and create group. The selecton of features and creaton of group based on teraton process. Due to teraton sometmes loss the meanng full data and not generate accurate cluster group. For the generaton of accurate and reeducaton of data loss varous authors used swarm ntellgence algorthm along wth clusterng algorthm. the swarm ntellgence gves varous algorthm such as ant colony optmzaton, partcle of swarm optmzaton, glow swarm algorthm beep algorthm and many more algorthm. n ths paper presents the revew of clusterng technque along wth swarm ntellgence. The swarm ntellgence derved one algorthm s called KANTS. The KANTS algorthm used for medcal data analyss, but t faced a problem of proper feature selecton and seed selecton. Keywords: - Clusterng Algorthm, KANTS, and Swarm Intellgence. effectve ways to generate realstc smulatons of swarms, and last, but not least, strateges and deas for organzng goal-orented groups of robots (so-called swarm -bots [6]. The dea of usng KANTS as an art tool s contextualzed wthn the swarm art research and artstc feld. KANTS s an SI clusterng algorthm based on ant colones and stgmergy. The term stgmergy descrbes a speces ablty to communcate va envronment, together wth procedures that modfy that same envronment. In KANTS, the ants are the nput data vectors and communcate va a sterodal square grd of vectors wth the same dmenson of the nput vectors. The ants move on the grd and update t the grd vectors are adjusted toward the nput vectors (ants that vst them. In addton, ants tend to move toward regons of the grd wth smlar vectors. When properly tuned, KANTS gudes the swarm to a selforganzed state n whch complex patterns of global behavor emerge [7]. Localty INTRODUCTION Swarm ntellgence s a revolutonary technque for solvng optmzaton problems that formerly took ts nspraton from the bologcal examples that can be observed n nature, such as ant colones, flocks of brds, fsh schools and bee hves, where a number of ndvduals wth lmted capabltes are able to come to ntellgent solutons for complex problems [9]. Swarm art s a type of generatve art, a term used to classfy artstc creatons that, wth a varyng degree of human nterventon, are generated by artfcal ntellgence systems or other computatonal models [6]. Swarm Intellgence (SI s a computatonal Paradgm that arose as a result of the developments n complexty studes. Typcally, SI systems rely on a populaton of smple enttes that nteract wth each other and/or wth the envronment by means of smple rules. The rules lead the system to a state n whch global patterns emerge.[2] Ths process s usually known as self-organzaton. swarm ntellgence based algorthms provde new ways to solve complex and dffcult optmzaton problems, new ways to manage and control traffc and communcatons networks, accomplshed and Collson Avodance Collectve Behavor Collectve Behavor Homogenety Flock Centerng Fgure 1: Man Prncples of Collectve Behavor of Swarms Cluster analyss, or clusterng, refers to a set of mathematcal technques for sortng observed data nto 120

2 groups so as to maxmze the smlarty of observatons wthn the same group and mnmze the smlarty of observatons across dfferent groups. These technques can be used to dscover assocatons and structures wthn a data set that may not have been known. Cluster analyss has been wdely used n the bologcal and socal scences to help defne classfcaton schemes or taxonomes. It has also been used to suggest new ways of descrbng a populaton n busness and marketng applcatons. Clusterng s a dvson of data nto groups of smlar objects. Each group, called cluster, conssts of objects that are smlar between themselves and dssmlar to objects of other groups. In other words, the goal of a good document clusterng scheme s to mnmze ntra-cluster dstances between documents, whle maxmzng nter-cluster dstances (usng an approprate dstance measure between documents. A dstance measure (or, dually, smlarty measure thus les at the heart of document clusterng [23]. Clusterng s the mportant step for many errands n machne learnng. Secton-II gves the nformaton swarm ntellgence. In secton III dscuss about related work n the area of swarm ntellgence and clusterng algorthm. In secton IV dscuss the problem of current scenaro of clusterng and fnally dscuss n secton V concluson and future scope. II. Swarm Intellgence In ths secton dscuss the swarm ntellgence technque used for the process of algorthm optmzaton such as clusterng, classfcaton and data regresson. Bascally swarm optmzaton algorthm derved from the bologcal anmals and kts. The behavor of kts and anmal are very nsprng behavor durng the selecton of path and foods. It also gves the nformaton about survval factor of process. Here dscuss two swarm algorthm one s ant colony system and other s partcle of swarm optmzaton. ANT COLONY SYSTEM ACO s an optmzaton algorthm nspred n the collectve foragng behavor of ants to fnd and explot the food source that s nearest to the nest. The desgn of an ACO algorthm mples the specfcaton of the followng aspects: A proper defnton of the problem that needs to be solved by the ants so that some strateges can be made to ncrementally buld soluton to the problem based on some transton rule, amount of pheromone on the path and some local nformaton. A method to construct vald solutons that can be consdered legally permssble n the real world stuaton.[3] A heurstc functon that should be desgned based on the problem to measure the sgnfcance of the tem/terms that can be added to the current soluton so as to gve rght drecton to the process of fndng optmal soluton. A rule for updatng the pheromones on certan paths that leads to good soluton to the problem and fne-tune the pheromone-tral. A transton rule that s based on the heurstc functon and amount of pheromone on a path to decde about the movement of ants and constructon of soluton. ARTIFICIAL ANTS Artfcal ants are characterzed as agents that mtate the behavor of real ants. However t should be noted that an artfcal ant system has some dfferences n comparson wth real ants whch are as follows: Artfcal ants have memory. They are not completely blnd. They follow a dscrete tme system. The Ant System works n two major steps: Constructon of the soluton to the problem under consderaton. Updatng the pheromone trals whch may ncrease or decrease the amount of pheromone on certan paths. Partcle of swarm optmzaton Partcle of swarm optmzaton s dynamc populaton based optmzaton technque. The dynamc populaton based selecton process of feature attrbute of network traffc data. The network traffc data categores nto dfferent secton of number of partcle. The number of partcle process as the number of attrbute are dstrbuted along wth rang of path. Some steps are Step 1: the process of feature attrbute n range and dstrbute and defne velocty of partcle. v = v + c 1 R 1 ( p p + c 2 R 2 ( g p Step 2: the process of velocty update of partcle accordng to ther teraton of each partcle agent set. (1 where p and v are the poston and velocty of partcle respectvely; p and g s the poston wth the objectve value found so far by partcle and the entre populaton respectvely; w s a parameter controllng the dynamcs of flyng; R1 and R2 are random varables n the range [0,1]; c1 and c2 are factors controllng the related weghtng of correspondng terms. The random varables support the PSO wth the ablty of stochastc searchng. Step 3: Poston updatng The postons of all partcles are updated accordng to, p = p + v (2 After updatng, p should be tested and lmted to the allowed range. 121

3 Step 4: Memory updatng Update p and gwhen condton s met, p g = p = g f f f ( p > f ( g > f ( p f ( g (3 wheref(x s the objectve functon to be optmzed. Step 5: Stoppng CondtonThe algorthm repeats steps 2 to 4 untl certan stoppng condtons are met, such as a predefned number of teratons. Just the once stopped, the algorthm reports the values of g and f(g as ts soluton. PSO utlzes several searchng ponts and the searchng ponts gradually get close to the global optmal pont usng ts p and g. Prelmnary postons of p and g are dfferent. However, usng thee dfferent drecton of p and g, all agents progressvely get close to the global optmum. III RELATED WORK Ths secton gves an extensve lterature survey on the exstng swarm ntellgence and ant colony optmzaton methods n the feld of data mnng specally for clusterng technque. They study varous research and journal paper related to swarm ntellgence and ant colony optmzaton along wth data mnng method. Carlos M. Fernandes, Antono M. Mora, Juan J. Merelo, Agostnho C. Rosa Et al. [1] In ths paper authors dscuss a smplfed verson of KANTS and descrbes recent experments wth the algorthm n the context of a contemporary artstc and scentfc trend called swarm art, a type of generatve art n whch swarm ntellgence systems are used to create artwork or ornamental objects. KANTS s used here for generatng color drawngs from the nput data that represent real-world phenomena, such as electroencephalogram sleep data. Manju,Chander Kant Et al. [2] Descrbes n ths paper the dea of Ant Colones s presented wth bref ntroducton to ts applcatons n dfferent areas of problem solvng n computer scence. Artfcal Swarm/Ant foragng utlzes varous forms of ndrect communcaton, nvolvng the mplct transfer of nformaton from agent to agent through modfcaton of the envronment. Usng ths approach, one can desgn effcent searchng methods that can fnd soluton to complex optmzaton problems. O.A. Mohamed Jafar, R. Svakumar Et al. [3] Dscuss on In ths paper, a bref survey on ant-based clusterng algorthms s descrbed. They also present some applcatons of ant-based clusterng algorthms. Most promsng among them are swarm ntellgence algorthms. Clusterng wth swarm-based algorthms s emergng as an alternatve to more conventonal clusterng technques..c. Fernandes, A.M. Mora, J.J. Merelo, V. Ramos, J.L.J. Laredo Et. al. [4] Authors presents n ths paper a new antbased method that takes advantage of the cooperatve selforganzaton of Ant Colony Systems to create a naturally nspred clusterng and pattern recognton method. The approach consders each data tem as an ant, whch moves nsde a grd changng the cells t goes through, n a fashon smlar to Kohonens Self-Organzng Maps. Davd A. Van Veldhuzen, Gary B. Lamont Et. al. [5] Dscuss here a mult objectve optmzaton problems and certan related concepts, present an MOEA classfcaton scheme, and evaluate the varety of contemporary MOEAs. Current MOEA theoretcal developments are evaluated; specfc topcs addressed nclude ftness functons, Pareto rankng, nchng, ftness sharng, matng restrcton, and secondary populatons. Erc Bonabeau Davd Corne, Rccardo Pol Et.al. [6] Authors n ths paper dscuss, swarm ntellgence based algorthms provde new ways to solve complex and dffcult optmzaton problems, new ways to manage and control traffc and communcatons networks, accomplshed and effectve ways to generate realstc smulatons of swarms, and last, but not least, strateges and deas for organsng goal-orented groups of robots (so-called swarm -bots for potental future applcatons rangng through agrculture, flexble manufacturng and space exploraton. Mehd Neshat,Ghodrat Sepdnam,Mehd Sargolzae, Adel Najaran Toos Et. al. [7] Present, a revew of AFSA algorthm and descrbes the evoluton of ths algorthm along wth all mprovements, ts combnaton wth varous methods as well as ts applcatons. Ths algorthm has many advantages ncludng hgh convergence speed, flexblty, fault tolerance and hgh accuracy. AFSA (artfcal fshswarm algorthm s one of the methods of optmzaton among the swarm ntellgence algorthms. Ths algorthm s nspred by the collectve movement of the fsh and ther varous socal behavors. Pradeep Jha, Krshan Kant Lavana, Deepak Dembla Et. al. [8] dscuss n ths paper how to reduce the executon tme of SVC procedure as well as to mprove the ablty of proposed SVC scheme n dealng wth classfcaton problems. The procedure contans a Ant colony optmzaton (ACO technque.they have used Ant Colony Optmzaton (ACO based data preprocessng step to remove nose ponts, outlers, and nsgnfcant ponts from the dataset Experments showed reducton n the executon tme of SVC procedure wthout alterng the fnal cluster confguratons. Usng ther proposed method, the classfcaton accuracy of all dataset s better than the SNN- SVC method. 122

4 A.M. Mora, L.J. Herrera, P.A. Castllo, J.J. Merelo Et. al. [9] Ths paper reports the nvestgatons and expermental procedures conducted for desgnng an automatc sleep classfcaton tool based solely n the features extracted wth wavelets from EEG, EMG and EOG sgnals, wthout any vsual ad or context-based evaluaton. Real data collected from patents was processed and classfed by several tradtonal and bo-nspred heurstcs. Stefan Bornhofen Vncent Gardeux, Andréa Machzaud Et. al. [10] Ths paper presents the swarm paradgm n the context of artstc creaton, and more partcularly explores the nterest of enhancng swarm models wth dynamcs nspred from natural ecosystems. they ntroduce an energy budget to the agents of a swarm system, and show how mappng the energy level to vsual nformaton such as lne wdth or color, combned wth mechansms such as resource chasng and consumpton, S. Dehur S.B. Cho Et. al. [11] Authors In ths paper, proposed a mult objectve Pareto based partcle swarm optmzaton (MOPPSO to mnmze the archtectural complexty and maxmze the classfcaton accuracy of a polynomal neural network (PNN. To support ths, they provde an extensve revew of the lterature on mult objectve partcle swarm optmzaton and PNN. IV PROBLEM STATEMENT For the purpose of medcal data clusterng varous machnes learnng algorthm are appled, such as clusterng, weghted clusterng, and regresson. Two of the most crtcal and well generalzed problems of medcal data are ts new evolved feature and concept-drft. Snce a medcal data s a fast and contnuous event, t s assumed to have nfnte length. Therefore, t s dffcult to store and use all the hstorcal data for tranng. The most dscover alternatve s an ncremental learnng technque. Several ncremental learners have been proposed to address ths problem [8], [7]. In addton, concept-drft occurs n the medcal when the underlyng concepts of the medcal change over tme. A varety of technques have also been proposed n the lterature for addressng concept-drft [2], [6], [7] n data medcal clusterng. However, there are two other sgnfcant characterstcs of data mult-categores, such as concept evoluton and feature evoluton that are gnored by most of the exstng technques. Concept-evoluton occurs when new classes evolve n the data. On the category process we found some mportant problem n cluster orented medcal data clusterng. These problems are gven below. 1. Medcal data clusterng suffered from multple feature evaluaton, 2. Selecton of number of cluster for mult-level [1]. 3. Dversty of feature selecton process [12]. 4. Boundary value of cluster. 5. Outler data treat as nose. 6. Number of teraton 7. Increase value of MSR 8. Cluster contan valdaton V CONCLUSION AND FUTURE WORK In ths paper resents the revew of clusterng technque along wth swarm ntellgence. The process of swarm ntellgence reduces the number of teraton and data loss. The swarm ntellgence gve verty of algorthm such as ant colony optmzaton, partcle of swarm optmzaton and many more kts based algorthm. on the bass of revew estmated that all authors used swarm ntellgence algorthm for the optmzaton of cluster features and reduces the number of teraton. But the analyss of medcal data faced a problem of uncertan nose. The processng of nose along wth these data creates a problem for the generaton of cluster. For the better generaton of cluster used partcle of swarm optmzaton technque. REFERENCES [1] Carlos M. Fernandes, Antono M. Mora, Juan J. Merelo, Agostnho C. Rosa KANTS: A Stgmergc Ant Algorthm for Cluster Analyss and Swarm Art IEEE 2014 PP [2] Manju,Chander Kant Ant Colony Optmzaton: A Swarm Intellgence based Technque Internatonal Journal of Computer Applcatons 2013 PP [3] O.A. Mohamed Jafar, R. Svakumar Ant -based Clusterng Algorthms: A Bref Survey Internatonal Journal of Computer Theory and Engneerng, 2010 PP [4] C. Fernandes, A.M. Mora, J.J. Merelo, V. Ramos,J.L.J. Laredo Kohon Ants: A Self-Organzng Ant Algorthm for Clusterng and Pattern Classfcaton [5] Davd A. Van Veldhuzen,Gary B. Lamont Multobjectve Evolutonary Algorthms: Analyzng the State-of-the-Art MIT 2000 PP [6] Erc Bonabeau,Davd Corne, Rccardo Pol Swarm ntellgence: the state of the art specal ssue of natural computng 2010 PP [7] Mehd Neshat,Ghodrat Sepdnam,Mehd Sargolzae, Adel Najaran Toos Artfcal fsh swarm algorthm: a survey of the state of- the-art, hybrdzaton, combnatoral and ndcatve applcatons Sprnger

5 [8]Pradeep Jha, Krshan Kant Lavana,Deepak Dembla Ant Colony Optmzaton Technque: Robust Data Preprocessng Procedure Support Vector Clusterng IJITKM 2013 PP [9] A.M. Mora, L.J. Herrera, P.A. Castllo, J.J. Merelo Sleepng wth Ants, SVMs, ANNs and SOMs. [10]Stefan Bornhofen, Vncent Gardeux, Andréa Machzaud From Swarm Art toward Ecosystem Art [11]S. Dehur S.B. Cho Mult-crteron Pareto based partcle swarm optmzed polynomal neural network for classfcaton: A revew and state-of-the-art Elsever 2009 PP