2016 International Congress on Computation Algorithms in Engineering (ICCAE 2016) ISBN: 978-1-60595-386-1
1 INTRODUCTION
Currently, there is a phenomenon in China. For phys-ical training aids, the professional athletes would like to select equipment produced in foreign countries, because there is certain training intensity difference about some equipment of between China and other foreign countries. However, for foreign equipment, we have no standardized using system in its performance and strength, so the scientific application method is needed. It is an obviously long journey for our bas-ketball players to go in comparison to the players in western countries. This situation is caused by many factors, but the key factor is the athletes training. The aids can be used to perfect major steps of training. The basketball training can make athletes grasp more per-fect basic technique. And these aids can make athletes’ training technique more adept, widen their training scale and increase their competitive resistance, so as to achieve the best results.
2 CLUSTERING ALGORITHM 2.1 Hierarchical clustering algorithm
The clustering is carried out through construction of hierarchy. The hierarchical clustering method is also known as the tree clustering method, solving the
structure of clusters by repeated division and aggrega-tion of data at last. The clustering process is finally divided into several categories to be solved through finding out the category with similar distance and hierarchical classification.
2.2 FCM clustering algorithm
Through the establishment of external indicators and internal indicators, the FCM clustering algorithm is calculated as follows:
(1) Get a certain stage and training data of an ath-lete to clear up at first;
(2) Develop the principle of division, and divide the data obtained in accordance with the developed prin-ciple of division of network flow;
(3) Extract data for matching;
(4) Analyze whether the data can match the current development trend;
(5) Otherwise, divide again.
3 APPLICATION OF BASKETBALL TRAINING AIDS BASED ON FCM CLUSTERING ALGO-RITHM
In the modeling process, determine the factors set at first:
Applied Research on Clustering Algorithm
in Basketball Training Aids
Shaoqing Liu1 & Yanhui Zhang2
1
Teaching and Research Office of Physical Education, Department of Public Basic Courses, Langfang Health Vocational College, Langfang, Hebei, China
2
Langfang Health Vocational College, Langfang, Hebei, China
ABSTRACT: The basketball training aids are used for basketball training to achieve a scientific and rational training effect. This paper analyzes the application of basketball aids based on the clustering algorithm, and mainly adopts the FCM clustering algorithm, and verifies by the results obtained from the hierarchical clustering algorithm, and analyzes the use ratio of various types of aids used in training according to the types and devel-opment prospect of basketball aids.
1 2
{ ,
,
,
n}
U
u u
u
Determine the judgment set:
1 2
{ ,
,
,
m}
V
v v
v
Determine the fuzzy evaluation matrix
R
( )
r
ij n m :11 12 1
21 22 2
1 2
n n
m m mn
r r r
r r r
R
r r r
First, make a judgment
( )(
1, 2,
, )
i
f u
i
n
foreach factor, so as to obtain a fuzzy mapping f from the factor set
U
to the judgment setV
, that is:1 2
: ( )
( ) ( , , , ) ( )
i i i i im
f U F U
u f u r r r F V
Then find out the fuzzy relation ( )
f
R F UV
from the fuzzy mapping
f
, that is:( , ) ( )( ) ( 1, 2, , ; 1, 2, , )
f i j i j ij
R u v f u v r i n j m
(4) The weight set is
1 2
( ,
,
,
n)
( )
A
a a
a
F U
;its boundary condition is:
1 1 0 n i i i a a
11 12 1
21 22 2
1 2 3
1 2
1 2 3
,
,
,
,
,
, ,
,
n n n
m m mn
n
B
A R
r
r
r
r
r
r
a a a
a
r
r
r
b b b
b
Comprehensive judgment: For the weight 1 2
( ,
,
,
n)
( )
A
a a
a
F U
, taking themaxi-mum--minimum compositional arithmetic by the use of model M( , ) , the comprehensive judgment can be obtained:
1
( ( ), 1, 2, , )
n
j i ij
i
B A R b a r j m
The determination of the weight A( ,a a1 2, ,an)
of the judgment set
V
is an important part in themodeling process. The main reason is that the judg-ment process based on reality is determined by estab-lishing a fuzzy relation.
The factors set
U
of cluster analysis is estab-lished:
1 2 3 4
U U U U U
Basketball training aids can be divided into five important factors: physical training aids (
1
U ), tech-nical training aids (
2
U ), mental training aids (U3), tactical training aids (
4
U ) and future development trends (
5
U ). They are obtained in Table 3. And this paper establishes a small factor set for five important factors.
The judgment set can be obtained by the above fac-tors.
1 11
,
12,
13,
14U
u
u
u
u
;U
2
u
21,
u
22,
u
23,
u
24
;
3 31
,
32U
u
u
; U4
u41,u42
;
5 51
,
52,
53U
u
u
u
In the solving process of the algorithm, the ranking matrix for the following five aspects can be obtained: physical training aids (
1
U ), technical training aids (
2
U ), mental training aids (U3), tactical training aids (
4
U ), future development trends (U5).
The weight vector can be obtained in the ranking process:
1,
2,
3,
4,
5
= 0.3, 0.3, 0.2, 0.1, 0.1
* T
i i
U
U
*
1
10
U
,U
2*
9.4
,U
3*
5.6
,U
4*
4
,U
5*
4
Via normalization processing:
*
1
0.303
U
,U
2*
0.285
,U
3*
0.170
,*
4
0.121
U
,U
5*
121
We can obtain:
0.303 0.285 0.170 0.121 0.121
A
This paper establishes a membership of judgment as shown in Table 2.
of the second-level indicators can be reflected by the size of score value. Table 3 can be obtained through above indicators.
This paper obtains a fuzzy set of weight factor of a single indicator:
*
1 11
,
12 13 140.42 0.22 0.23 0.15
U
U U
,
U
,
U
， ， ，
*
2 21, 22, 23, 24 0.54 0.1 0.24 0.14
U U U U U ， ， ，
*
3 31, 32 0.4 0.6
U U U ，
*
4 41, 42 0.55 0.45
U U U ，
*
5 51, 52, 53 0.42, 0.32, 0.26
U U U U
After calculation, the evaluation set of the following aspects can be obtained: physical training aids (U1), technical training aids (U2), mental training aids (U3), tactical training aids (U4) and future develop-ment trends (U5).
[image:3.516.45.457.67.193.2]Physical training aids:
Table 1. Index evaluation system for application of basketball training aids.
Physical training aids (U1) Technical train-ing aids (U2) Mental training aids (U3)
Tactical training aids (U4)
Future development trends (U5) Elastic strap training u11 Shooting training
aid u21
Mental computer
31
u Multimedia combina-tion equipment u41 Equipment utilization rateu51
Kinetic drogue chute training u12
Ball-pass training aids u22
Questionnaire
survey u32 Tactical research 42
u
Research on scientific research innovation u52Agility ladder training u13
Anti-bow training glasses
u
23Equipment utilization ratio u53
Core power equipment 14
[image:3.516.45.452.233.314.2]u Human defense model
u
24Table 2. Membership of judgment in application of basketball training aids.
Judgment method Set score range
0-60 60-80 80-90 90-100
Very good 0 0 0.05 0.95
Good 0 0.05 0.9 0.05
General 0.05 0.9 0.05 0
Bad 0.95 0.05 0 0
Table 3. Evaluation value of application for basketball training aids.
Cluster indices value Each indicator value
Elastic strap training u11 very
good Mental computer 31
u good
Kinetic drogue chute trainingu12 general Questionnaire surveyu32 good
Agility ladder training
u
13 verygood Multimedia combination equipmentu41 good Core power equipmentu14 very
good Tactical researchu42 general
Shooting training aidu21 general Equipment utilization rateu51 good
Ball-pass training aidsu22 very
good Research on scientific research innovation 52
u
general [image:3.516.51.449.355.517.2]1
0 0 0.05 0.95
0 0 0.05 0.95
=
0 0 0.05 0.95
0 0.05 0.9 0.05
U
Technical training aids:
2
0 0 0.05 0.95
0 0 0.05 0.95
=
0 0 0.05 0.95
0 0.05 0.9 0.05
U
Mental training aids:
3
0 0 0.05 0.95
=
0 0.05 0.9 0.05
U
Tactical training aids:
3
0 0 0.05 0.95
=
0 0 0.05 0.95
U
Future development trends:
2
0 0 0.05 0.95
= 0 0.05 0.9 0.05
0 0.05 0.9 0.05
U
According to the formula:
i i i
B
A R
The fuzzy evaluation matrix can be obtained after normalization processing ofBi:
1
2
3
4
5
0.07 0.26 0.14 0.41
0 0.16 0.74 0.54
0.14 0.14 0.31 0.17 0.16 0.21 0.31 0.34 0.11 0.32 0.26 0.31 B B B B B B
The application status of the basketball training equipment aids can be obtained:
*
0.38 0.23 0.11 0.10 0.18
Z U B
0.38 ranks the first place in the evaluation value, so the evaluation indicator is very good. In some profes-sional exercise trainings, the application of basketball training aids in China has obtained an enough utiliza-tion ratio.
4 TEST MODEL BASED ON HIERARCHICAL CLUSTERING
First, calculate the judgment matrix:
(
ij n n)
Ak
k
,Establish the weight vector according to above steps:
k1,
k2,
k3, ,
kn
(
1, 2,
)
wk
w
w
w
w
k
x
Where,
k
represents one of the experts;x
repre-sents the total number of experts;j
represents an indicator in a target layer;n
represents the total number of indicators in a target layer.By the formula:
'
1 2
f f fs
W j
W
W
k W
The normalization processing is given to the geo-metric mean of the weight vector. According to the formula: ' ' 1 j n j
w f
w
W f
Therefore, the total ranking list of the layer can be obtained through the weight composed by '
W j
, as shown in Table 4.By comparing with the weight values obtained by FCM clustering method, two clustering methods have similar structures in the first-level indicators due to the second-level indicators.
5 CONSISTENCY CHECK
This paper tests the consistency of indicators estab-lished in the FCM clustering method and the hierar-chical clustering method by the use of consistency indicators, which are physical training aids, technical training aids, mental training aids, tactical training aids and future development trends. According to the formula: max
1
n
CI
n
Where: max and n are respectively the maximum eigenvalue and order of the comparison matrix.
For the eigenvalues obtained from the hierarchical clustering process,
(0)
max 4.073,RI 0.9
4.073 4 0.24 4 1
CI
0.024
0.027 0.1 0.90
CI CR
RI
Corresponding to Table 5, the consistency check is valid.
6 CONCLUSION
The basketball training aids can not only greatly en-hance the training effect, but also provide safety guarantee for the athletes to achieve scientific and reasonable training effect. First, this paper divides into the basketball training aids: physical training aids, technical training aids, mental training aids and
tacti-cal training aids. This paper analyzes the application of basketball training aids and utilization ratio based on the clustering algorithm, and establishes the hier-archical clustering model for comparison validation of results.
REFERENCES
[1] Sun Jigui, Liu Jie, & Zhao Lianyu. 2008. Research on clustering algorithm. Journal of Software. (01): 48-61. [2] Zhou Xiang. 2014. Applied research on China’s
[image:5.516.59.458.80.376.2]basket-ball training aids. Chengdu: Chengdu Sport University. [3] Zhang Chen, Xia Shixiong, & Liu Bing. 2011. An im-proved fuzzy clustering algorithm. Applied research of computers, 28 (8): 2848-2851.
Table 4. Total ranking list of the layer.
B1 B2 B3 B4 B5
Total ranking result of the layer C 0.3 0.29 0.17 0.13 0.11
Elastic strap training C11 0.42 0.126
Kinetic drogue chute trainingC12 0.21 0.063
Agility ladder trainingC13 0.22 0.066
Core power equipmentC14 0.15 0.045
Shooting training aidC21 0.49 0.142(max)
Ball-pass training aidsC22 0.12 0.035
Anti-bow training glassesC23 0.21 0.061
Human defense modelC24 0.18 0.052
Mental computerC31 0.42 0.071
Questionnaire surveyC32 0.58 0.098
Multimedia combination equipmentC41 0.45 0.059
Tactical researchC42 0.55 0.072
Equipment utilization rateC51 0.42 0.0462
Research on scientific research innovationC52 0.33 0.0363
Equipment utilization ratioC53 0.25 0.0275(
min
)Table 5. RI value.
n 1 2 3 4 5 6 7 8 9 10 11
[4] Gu Chen. 2007. Development status of China’s high-end sports equipment and development countermeasure re-search. East China Normal University.
[5] Dai Dexiang. 2012. Practice research on antagonistic training of basketball center with instruments. Fight • Sport Forum, 4 (10).
[6] Wang Baocheng, Kuang Lubin, & Tan Zhenbin, et al. 2001. Basic Theory and Content of Physical Training for Basketball Players. Journal of Capital Institute of Phys-ical Education, 13 (3): 38.
