Application of K-Means Clustering Algorithm for Classification of NBA Guards

Transcription

1 Applcato of K-Meas Clusterg Algorthm for Classfcato of NBA Guards Lbao ZHANG Departmet of Computer Scece, Shadog Uversty of Scece ad Techology, Qgdao , Cha Pgpg GUO Departmet of Computer Scece, Shadog Uversty of Scece ad Techology, Qgdao , Cha Famg LU Departmet of Computer Scece, Shadog Uversty of Scece ad Techology, Qgdao , Cha A LIU Departmet of Computer Scece, Shadog Uversty of Scece ad Techology, Qgdao , Cha Cog LIU * Departmet of Computer Scece, Shadog Uversty of Scece ad Techology, Qgdao , Cha Abstract: I ths study, we dscuss the applcato of K-meas clusterg techque o classfcato of NBA guards, cludg determato category umber, classfcato results aalyss ad evaluato about result. Based o the NBA data, usg rebouds, asssts ad pots as clusterg factors to K-Meas clusterg aalyss. We mplemet a mproved K-Meas clusterg aalyss for classfcato of NBA guards. Further expermetal result shows that the best sample classfcato umber s sx accordg to the mea square error fucto evaluato. Depedg o K-meas clusterg algorthm the fal classfcato reflects a objectve ad comprehesve classfcato, objectve evaluato for NBA guards. Keywords: K-Meas clusterg algorthm, NBA guards, classfcato umber 1. INTRODUCTION I ths study, K-meas clusterg techque s appled to the classfcato ad evaluato for NBA guards. Recetly, the classfcato of NBA guards s maly based o the startg leup, tme, pots ad reboudg [10]. Meawhle, startg pot guard, reserve guard, pot guard ad offesve guard are also frequetly used tradtoal classfcato methods. Accordg to tradtoal classfcato methods, researchers eeded to assg classfcato threshold to each dcator maually, whch was so subjectve that some partcular players could ot be classfed a logc stuato. I ths study, K-Meas clusterg techque org s from mache learg feld s appled to the classfcato of NBA guards. I order to realze the objectve ad scetfc classfcato of NBA guards, ths study depeds o NBA seaso guards data whch s stadardzed ad processed by mathematcal models ad Java laguage. I ths way, the guards type could be defed scetfcally ad properly based o classfcato result. Meawhle, the guards fucto the team could be evaluated farly ad objectvely. K-meas clusterg ad mprovemets s wdely used preset study, such as etwork truso detecto [3], mage segmetato [4], ad customer classfcato [5] ad so o. A cluster aalyss of NBA players are very commo, but ther works maly focus o the posto of players. 2. K-MEANS APPLICATION Cluster aalyss s the task of groupg a set of objects such a way that objects the same group (called a cluster) are more smlar ( some sese or aother) to each other tha to those other groups. It s a mportat huma behavor. K- meas algorthm [1, 2] s the most classc dvso-based clusterg method, s oe of the te classcal data mg algorthms. The basc dea of K-meas algorthm s: k pot the space as the cluster cetrods to cluster, classfy ther closest objects [8]. Through a teratve approach, each successve update the value of cluster cetrods utl get the best clusterg results so that the obtaed clusterg satsfy objects the same cluster have hgh smlarty ad at the same tme objects the dfferet cluster have low smlarty. Therefore, based o K-meas clusterg algorthm oe ca detfy the guard s fucto the team, ad helps people to obta a objectve evaluato about guard s ablty. 2.1 K-meas models establshmet Data flterg ad processg The data of tables obtaed from DATA-NBA ( as show Table 2-1, As the ma task of guard s the score, rebouds ad asssts, so we ca select these three data tems as data factors for dstace calculato of clusterg aalyss. I addto, asssts ad score are dfferet, a player 10 asssts the dffculty, ot less tha 20 pots, f ot to take the measures stadard, the cluster wll ot be far, the score wll become the ma dcator, ad rebouds ad asssts wll become a secodary dcator. So we use the followg equato to deal wth the data processg. =1 P the orgal value of the player C* * P SP = P SP the stadard value of the player C auxlary parameter for data amplfcato the total umber of players a dataset 1

2 2.1.2 K-meas algorthm defect K-meas algorthm has some drawbacks [4]: Frst, the umber of k cluster ceters eed to be gve advace, but practce the selected k value s very dffcult to estmate. It s extremely dffcult to kow how may types of data collecto Iteratoal Joural of Scece ad Egeerg Applcatos Table 2-1: The part of the orgal data should be dvded advace. Secod, K-meas eed to artfcally determe the tal cluster ceters, dfferet tal cluster ceters may lead to a completely dfferet clusterg results. Cosderg the frst defect, we eed to evaluate dfferet values of k the k meas clusterg, ad select the most reasoable k value. Cosderg the secod defect, we choose the tal ceter pot by the remote-frst algorthm [9]. The basc dea of the tal clusterg ceter pot les : the tal clusterg ceters should be as far as possble from the dstace betwee each other. Detaled steps of the k clusterg ceter wth remote-frst algorthm s explaed as follows: Step1: Choose oe ceter uformly radomly from the data pots. Step2: For each data pot x, compute D(x), the dstace betwee x ad the earest ceter that has already bee chose. Step3: Choose oe ew data pot radomly as a ew ceter, usg a weghted probablty dstrbuto where a pot x s chose wth probablty proportoal to D(x) 2. Step4: Repeat Steps 2 ~ 3 utl k ceters have bee chose. 2.2 K-meas algorthm Data Preparato I order to costruct the K-meas model, oe eeds to get the seaso NBA guard data whch cludes 120 NBA guards data. We stadardze ad flter the data, to prepare for the K-meas aalyss. The fltered data s stored csv fle & a excerpt of our processed data s show Table 2-2. Table 2-2: 120 NBA Guard Regular Seaso Data Player Team Rebouds Asssts Scores 1 Russell - Westbrook Thuder James - Harde Rockets Stephe Curry Warrors Kobe Bryat Lakers Carey - Owe Cavalers

3 6 Kle - Thompso Warrors Dwyae - Wade Heat Dama - Lllard Tral Blazers DeMar - DeRoza Raptors Kev - Mart Tmberwolves Chrs Paul Clppers Isaah - Thomas Celtcs Mota - Ells Mavercks Jose - Caldero Kcks Jaso - Rchardso 76ers Qucy - Podexter Pelcas Boja - Bogdaovch Nets Marcus - Thorto Celtcs Algorthm Desg Usg K-meas clusterg algorthm for data aalyss. The basc dea of K-meas algorthm [11] s: allocatg data set D to k clusters. To determe k clusters, we eed to determe the k ceter C1, C2 Ck, calculate the dstace to each pot to the ceter for each pot sde dataset, the pot that the shortest dstace from the ceter classfed as represeted by clusters. K-meas algorthm steps are explaed follows: Step1: Determe the umber of K-meas clusterg ceter k; Step2: The use of remote-frst algorthm to talze the ceter of k; Step3: The pots of dataset D assged to the earest ceter, formg a k clusters; Step4: The calculato k Category cluster cetrod obtaed by [3], the earest pot of dataset D from the cetrod as the ew ceter; Step5: Repeat [3] ~ [4], utl the ceter rema stable. Eucldea dstace s calculated as follows: k jk D = (P - P ) k=1 k jk D = the dstace betwee P ad P P = thevalueof P P = thevalueof P K value determato After calculato the results of the k are 2, 3, 5, 6, 7, ad 8 by the k-meas algorthm, ad the we use the Mea Squared Error to perform the comparso of results wth dfferet k values. The calculato formula s as follows: 2 (P - PC ) =1 MSE = = the total umber of pot a dataset C = the umbers of clusterg ceter P =the pot PC = theceter of the pot MSE = the mea squared error 2 j j 3

4 Accordg to Fgure 2-1 ad Table 2-2, we ca see that as k- values gradually crease from 2 to 8, the mea square error gettg smaller ad smaller. Clusterg result also gradually chaged for the better, ad the small chages of clusters to acheve a relatvely stable state whe the ceter pots surpass sx. Ths s the mmum mea squared error, t ca be cocluded that whe the cluster umber s 6, the mea squared error s becomg smaller, the smlarty wth the class s hgher, ad classfcato result s the best at the same tme. Table 2-2: Mea Square Error for Dfferet Values of Tme K-Values Mea square error Fg2-1: The Mea Square Error Varato Dagram 3. RESULTS ad EVALUATION 3.1 Classfcato Results Based o the above aalyss that the effect of clusterg s the best whe k = 6, the NBA guard ca be dvded to 6 categores, the classfcato results s show Table 3-1, classfcato ad aalyss of the results are as follows: Category 1: The guards whose assst ad score s well are the ma shootg ad pot of the team. However, lmted playg tme, the data s ot partcularly outstadg, such as Mau Gobl, Toy Parker. Category 2: The guards whose score ablty ad rebouds ablty are hgh, wth more playg tme, s absolutely super guard ad the core of the team, such as Harde, Curry ad Westbrook. Category 3: The guards whose score ablty ad rebouds ablty are outstadg, asssts ablty s ormal, are the guards of the Swgma type. They ca make eough cotrbuto to the team's defese ad offese, such as Ima - Shumpert, Wesley - Matthews, etc. Category 4: The guards who get pots ad 8.27 asssts, are typcally asssts madma, the tator of the offese, the core ad leader for a team such as Chrs - Paul, Joh -Wall. Category 5: The guards whose score ablty are much hgher tha rebouds ablty ad asssts ablty, should be the team's pot guard, the team's playmaker such as Wade, Owe. Category 6: Compared rebouds ablty ad asssts ablty, score ablty s the ma cotrbuto of ths category guard, usually as the team's backup pot guard, the outstadg ablty of sgles or log shot well, such as J.J-Redck, Nck - Youg. 4

5 Table 3-1: K-Meas Clusterg Result Classfcato Number Category Cetrod Rebouds Asssts Scores Category members(separated betwee ames) Isaah - Thomas Brado - Jegs Toy - Toy Parker Morrs - Wllams Erc - Gordo Terre - Burke Brado - Kght Ismal - Smth Jarrett - Jack Morrs - Wllams Jeremy JR Smth DJ- August Mau Gobl - Maro Chalmers Zach - Lav CJ Watso Des - Schroeder Jameel-Nelso DJ- August Gray Davs - Vasquez Jose - Caldero Russell - Westbrook James - Harde - Stephe Curry - Kobe Bryat Dama-Lllard Kyle - Lor Erc - Bledsoe Tyreke - Evas Mchael - Carter - Wllams Kev - Mart Wesley-Matthews Brad - Bll Aaro - Afflalo Avery-Bradley Alec - Burks Shabazz- Muhammad A Log - Afflalo JR Smth Pop Do - Waters Rodey - Stuckey Be - Mark Lehmer Gerald- Hederso Jorda-Clarkso Lagsto- Galloway -Gary Neal Wll-Barto Patrck- Beverly Waye-Ellgto Imra-Shumpert Jaso- Rchardso Qucy-Podexter Chrs Paul Regge Jackso Joh Wall Jeff - Teague Thalad - Lawso Zhu-Huo Led Rcky - Rubo Rajo- Rodo Dero Wllams - Wllams Carey - Owe Dwyae-Wade Kle - Thompso DeMar-DeRoza Isaah - Thomas Mota-Ells Vctor- Oladpo Brado - Kght Derek - Ross Kemba - Walker Morrs - Wllams Toy - Rothe Gola - Dragc Darre - Collso George - Hll - Mke Coley Alec Frak - She Wede Joe - Johso Eva - Turer J.J. Redck Jamal - Crawford Lous - Wllams Nck - Youg Isaah - Buchaa Avo - Fourer Do - Waters Aaro - Brooks Tm - Hardaway II OJ- Mayo Jod - Meeks Athoy - Morrow Aaro - Afflalo AJ- Prce Alec Frak - She Wede Courtey - Lee - Gary Neal Alec Frak - She Wede Norrs - Cole Terrece - Rose - Gary Neal Marco Marco Belell Isaah - Buchaa Boja - Bogdaovch Marcus - Thorto 3.2 Aalyss ad Evaluato I ews ad meda, guards are dvded to pot guard ad shootg guard accordg to the arragemet the team, ad dvded to key guard ad reserve guard accordg to playg tme order. Therefore, geeral guard has four categores: key pot guard, key shootg guard, reserve pot guard& reserve shootg guard. However, basketball s the athletc sports of costat adjustmet ad adaptato. Throughout the league process, every NBA guard assgmet, as well as playg tme, playg order requred to make specfc arragemets accordg to eeds of the team ad coach s strategy. Therefore, ths tutve classfcato s depedet o people's subjectve judgmet whch s lmted based & chagg. Because guards fucto the game would costatly adjustmet, classfcato of guards should costatly adaptato, whch caused a great dsturbace to classfcato ad evaluato of NBA guards macroscopcally. Accordgly, the above classfcato ad evaluato methods heavly deped o so may subjectve factors, that the classfcato ad evaluato of NBA guards are ether scetfc or objectve. 5

6 I ths study, the K-Meas clusterg aalyss s appled to the classfcato of NBA guards. We take fully advatage of the statstcal data of NBA guards to aalyze data ad stadardze data ratoally. Mg the authetc classfed formato, wll get classfcato of NBA guards more scetfcally ad objectvely. Fd guards the team's role, the ablty to guards ad defeder the team's performace has a comprehesve uderstadg ad evaluato. Idetfy the guard s fucto the team, ca help people have a 4. CONCLUSIONS Tradtoally, clusterg s vewed as a usupervsed learg method for data aalyss. I ths study, we proposed a smple ad qualtatve methodology to classfy NBA guards by k- meas clusterg algorthm ad used the Eucldea dstace as a measure of smlarty dstace. We demostrated our research usg k-meas clusterg algorthm ad120 NBA guards data. Ths model mproved some lmtatos, such as maual classfcato of tradtoal methods. Accordg to the exstg statstcal data, we classfy the NBA players to make the classfcato ad evaluato objectvely ad scetfcally. Expermeted results show that ths methodology s very effectve ad reasoable. Therefore, based o classfcato result the guards type could be defed properly. Meawhle, the guards fucto the team could be evaluated a far ad objectve maer. 5. REFERENCES [1] Jawe Ha. Data Mg Cocepts ad Techques [M].Beg: Mechacal Idustry Press [2] [3] Lu Chag Qa. K-meas algorthm mprovemets ad etwork truso detecto applcato [J]. Computer smulato [4] Ya Xge.ISODATA ad fuzzy K-meas algorthm appled mage segmetato [C]. Chese Optcal Socety 2004 Academc Coferece. comprehesve uderstadg ad objectve evaluato about guard s ablty ad ther performace has a comprehesve uderstadg ad evaluato. Idetfyg the guard s fucto the team could help NBA Sports News, NBA commetator ad Basketball ethusasts have a comprehesve uderstadg ad objectve evaluato about guard s ablty ad ther performace. Furthermore,the classfcato results propose a effectve soluto for aalyss the extremely bg of NBA data, rather tha just make statstcal comparsos. [5]Qu Xaog.K-meas clusterg algorthm commercal bakg customers classfcato [J]. Computer smulato [6] Raymod T. Ng ad Jawe Ha, CLARANS: A Method for Clusterg Objects for Spatal Data Mg, IEEE TRANSACTIONS ONKNOWLEDGE ad DATA ENGINEERING [7] Zhu Xa based o smulated aealg Partcle Swarm Optmzato techques of geetc data bclusterg research [M]. Najg Normal Uversty [8]Y Z.D.Based collaboratve flterg Trusted Servce Selecto [M]. Najg Uversty of Posts ad Telecommucatos [9] Jagwe Ru. Dstrbuted mache learg framework based o cloud [M]. Xame Uversty [10] Data Source: http: // [11] Su Jgu, Lu Je, Zhaola Yu clusterg algorthm [J] Joural of Software [12] J Mg. Optmzato Selecto ad Evaluato of Techcal Idex Classfcato of NBA Elte Guard of. Cha Sport Scece ad Techology [13] Rchard J. Roger, Mchael W. Geatz, Data Mg a tutoral-based prmer, Addso-Wesley, [14] Josef Chlar, Rasm Latfovc, Jea Beaube. A Comparso Of Clusterg Strateges For Usupervsed Classfcato, Caada Joural of Remote Ses. 6