机器学习_Breast Cancer Wisconsin (Diagnostic) Data Set((诊断)数据集)
Breast Cancer Wisconsin (Diagnostic) Data Set((诊断)
数据集)
数据摘要：
Diagnostic Wisconsin Breast Cancer Database
中文关键词：
机器学习,多变量,分类,UCI,威斯康星,乳腺癌,
英文关键词：
Machine Learning,MultiVarite,Classification,UCI,Breast
Cancer,Wisconsin,
数据格式：
TEXT
数据用途：
Classification, Regression
数据详细介绍：
Breast Cancer Wisconsin (Diagnostic) Data Set
Abstract: Diagnostic Wisconsin Breast Cancer Database
Source:
Creators:
1. Dr. William H. Wolberg, General Surgery Dept.
University of Wisconsin, Clinical Sciences Center
Madison, WI 53792
wolberg '@' https://www.wendangku.net/doc/b87715648.html,
2. W. Nick Street, Computer Sciences Dept.
University of Wisconsin, 1210 West Dayton St., Madison, WI 53706
street '@' https://www.wendangku.net/doc/b87715648.html, 608-262-6619
3. Olvi L. Mangasarian, Computer Sciences Dept.
University of Wisconsin, 1210 West Dayton St., Madison, WI 53706
olvi '@' https://www.wendangku.net/doc/b87715648.html,
Donor:
Nick Street
Data Set Information:
Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at [Web Link]
Separating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree Construction Via Linear Programming." Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3
separating planes.
The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].
This database is also available through the UW CS ftp server:
ftp https://www.wendangku.net/doc/b87715648.html,
cd math-prog/cpo-dataset/machine-learn/WDBC/
Attribute Information:
1) ID number
2) Diagnosis (M = malignant, B = benign)
3-32)
Ten real-valued features are computed for each cell nucleus:
a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry
j) fractal dimension ("coastline approximation" - 1)
Relevant Papers:
First Usage:
W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on Electronic Imaging: Science and Technology, volume 1905, pages 861-870, San Jose, CA, 1993.
[Web Link]
O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), pages 570-577, July-August 1995.
[Web Link]
Medical literature:
W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) 163-171.
[Web Link]
W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Image analysis and machine learning applied to breast cancer diagnosis and prognosis. Analytical and Quantitative Cytology and Histology, Vol.
17 No. 2, pages 77-87, April 1995.
W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. Computerized breast cancer diagnosis and prognosis from fine needle aspirates. Archives of Surgery 1995;130:511-516. [Web Link]
W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. Computer-derived nuclear features distinguish malignant from benign breast cytology. Human Pathology, 26:792--796, 1995. [Web Link]
数据预览：
842302,M,17.99,10.38,122.8,1001,0.1184,0.2776,0.3001,0.1471,0.2419,0.0 7871,1.095,0.9053,8.589,153.4,0.006399,0.04904,0.05373,0.01587,0.0300 3,0.006193,25.38,17.33,184.6,2019,0.1622,0.6656,0.7119,0.2654,0.4601,0. 1189
842517,M,20.57,17.77,132.9,1326,0.08474,0.07864,0.0869,0.07017,0.1812, 0.05667,0.5435,0.7339,3.398,74.08,0.005225,0.01308,0.0186,0.0134,0.013 89,0.003532,24.99,23.41,158.8,1956,0.1238,0.1866,0.2416,0.186,0.275,0.0 8902
84300903,M,19.69,21.25,130,1203,0.1096,0.1599,0.1974,0.1279,0.2069,0.0 5999,0.7456,0.7869,4.585,94.03,0.00615,0.04006,0.03832,0.02058,0.0225, 0.004571,23.57,25.53,152.5,1709,0.1444,0.4245,0.4504,0.243,0.3613,0.08 758
84348301,M,11.42,20.38,77.58,386.1,0.1425,0.2839,0.2414,0.1052,0.2597, 0.09744,0.4956,1.156,3.445,27.23,0.00911,0.07458,0.05661,0.01867,0.059 63,0.009208,14.91,26.5,98.87,567.7,0.2098,0.8663,0.6869,0.2575,0.6638,0 .173
84358402,M,20.29,14.34,135.1,1297,0.1003,0.1328,0.198,0.1043,0.1809,0. 05883,0.7572,0.7813,5.438,94.44,0.01149,0.02461,0.05688,0.01885,0.017 56,0.005115,22.54,16.67,152.2,1575,0.1374,0.205,0.4,0.1625,0.2364,0.076 78
843786,M,12.45,15.7,82.57,477.1,0.1278,0.17,0.1578,0.08089,0.2087,0.07 613,0.3345,0.8902,2.217,27.19,0.00751,0.03345,0.03672,0.01137,0.02165, 0.005082,15.47,23.75,103.4,741.6,0.1791,0.5249,0.5355,0.1741,0.3985,0.1 244
844359,M,18.25,19.98,119.6,1040,0.09463,0.109,0.1127,0.074,0.1794,0.05 742,0.4467,0.7732,3.18,53.91,0.004314,0.01382,0.02254,0.01039,0.01369, 0.002179,22.88,27.66,153.2,1606,0.1442,0.2576,0.3784,0.1932,0.3063,0.0 8368
84458202,M,13.71,20.83,90.2,577.9,0.1189,0.1645,0.09366,0.05985,0.2196 ,0.07451,0.5835,1.377,3.856,50.96,0.008805,0.03029,0.02488,0.01448,0.0 1486,0.005412,17.06,28.14,110.6,897,0.1654,0.3682,0.2678,0.1556,0.3196 ,0.1151
844981,M,13,21.82,87.5,519.8,0.1273,0.1932,0.1859,0.09353,0.235,0.0738 9,0.3063,1.002,2.406,24.32,0.005731,0.03502,0.03553,0.01226,0.02143,0. 003749,15.49,30.73,106.2,739.3,0.1703,0.5401,0.539,0.206,0.4378,0.1072 84501001,M,12.46,24.04,83.97,475.9,0.1186,0.2396,0.2273,0.08543,0.203, 0.08243,0.2976,1.599,2.039,23.94,0.007149,0.07217,0.07743,0.01432,0.01 789,0.01008,15.09,40.68,97.65,711.4,0.1853,1.058,1.105,0.221,0.4366,0.2 075
845636,M,16.02,23.24,102.7,797.8,0.08206,0.06669,0.03299,0.03323,0.152 8,0.05697,0.3795,1.187,2.466,40.51,0.004029,0.009269,0.01101,0.007591, 0.0146,0.003042,19.19,33.88,123.8,1150,0.1181,0.1551,0.1459,0.09975,0. 2948,0.08452
84610002,M,15.78,17.89,103.6,781,0.0971,0.1292,0.09954,0.06606,0.1842, 0.06082,0.5058,0.9849,3.564,54.16,0.005771,0.04061,0.02791,0.01282,0.0 2008,0.004144,20.42,27.28,136.5,1299,0.1396,0.5609,0.3965,0.181,0.3792 ,0.1048
846226,M,19.17,24.8,132.4,1123,0.0974,0.2458,0.2065,0.1118,0.2397,0.07 8,0.9555,3.568,11.07,116.2,0.003139,0.08297,0.0889,0.0409,0.04484,0.01 284,20.96,29.94,151.7,1332,0.1037,0.3903,0.3639,0.1767,0.3176,0.1023 846381,M,15.85,23.95,103.7,782.7,0.08401,0.1002,0.09938,0.05364,0.1847 ,0.05338,0.4033,1.078,2.903,36.58,0.009769,0.03126,0.05051,0.01992,0.0 2981,0.003002,16.84,27.66,112,876.5,0.1131,0.1924,0.2322,0.1119,0.2809 ,0.06287
84667401,M,13.73,22.61,93.6,578.3,0.1131,0.2293,0.2128,0.08025,0.2069, 0.07682,0.2121,1.169,2.061,19.21,0.006429,0.05936,0.05501,0.01628,0.01 961,0.008093,15.03,32.01,108.8,697.7,0.1651,0.7725,0.6943,0.2208,0.359 6,0.1431
84799002,M,14.54,27.54,96.73,658.8,0.1139,0.1595,0.1639,0.07364,0.2303 ,0.07077,0.37,1.033,2.879,32.55,0.005607,0.0424,0.04741,0.0109,0.01857, 0.005466,17.46,37.13,124.1,943.2,0.1678,0.6577,0.7026,0.1712,0.4218,0.1 341
848406,M,14.68,20.13,94.74,684.5,0.09867,0.072,0.07395,0.05259,0.1586, 0.05922,0.4727,1.24,3.195,45.4,0.005718,0.01162,0.01998,0.01109,0.0141
,0.002085,19.07,30.88,123.4,1138,0.1464,0.1871,0.2914,0.1609,0.3029,0.0 8216
84862001,M,16.13,20.68,108.1,798.8,0.117,0.2022,0.1722,0.1028,0.2164,0 .07356,0.5692,1.073,3.854,54.18,0.007026,0.02501,0.03188,0.01297,0.016 89,0.004142,20.96,31.48,136.8,1315,0.1789,0.4233,0.4784,0.2073,0.3706, 0.1142
849014,M,19.81,22.15,130,1260,0.09831,0.1027,0.1479,0.09498,0.1582,0.0 5395,0.7582,1.017,5.865,112.4,0.006494,0.01893,0.03391,0.01521,0.0135 6,0.001997,27.32,30.88,186.8,2398,0.1512,0.315,0.5372,0.2388,0.2768,0.0 7615
8510426,B,13.54,14.36,87.46,566.3,0.09779,0.08129,0.06664,0.04781,0.18 85,0.05766,0.2699,0.7886,2.058,23.56,0.008462,0.0146,0.02387,0.01315,0 .0198,0.0023,15.11,19.26,99.7,711.2,0.144,0.1773,0.239,0.1288,0.2977,0.0 7259
8510653,B,13.08,15.71,85.63,520,0.1075,0.127,0.04568,0.0311,0.1967,0.0 6811,0.1852,0.7477,1.383,14.67,0.004097,0.01898,0.01698,0.00649,0.016 78,0.002425,14.5,20.49,96.09,630.5,0.1312,0.2776,0.189,0.07283,0.3184,0 .08183
8510824,B,9.504,12.44,60.34,273.9,0.1024,0.06492,0.02956,0.02076,0.181 5,0.06905,0.2773,0.9768,1.909,15.7,0.009606,0.01432,0.01985,0.01421,0. 02027,0.002968,10.23,15.66,65.13,314.9,0.1324,0.1148,0.08867,0.06227,0 .245,0.07773
8511133,M,15.34,14.26,102.5,704.4,0.1073,0.2135,0.2077,0.09756,0.2521, 0.07032,0.4388,0.7096,3.384,44.91,0.006789,0.05328,0.06446,0.02252,0.0 3672,0.004394,18.07,19.08,125.1,980.9,0.139,0.5954,0.6305,0.2393,0.466 7,0.09946
851509,M,21.16,23.04,137.2,1404,0.09428,0.1022,0.1097,0.08632,0.1769,0 .05278,0.6917,1.127,4.303,93.99,0.004728,0.01259,0.01715,0.01038,0.010 83,0.001987,29.17,35.59,188,2615,0.1401,0.26,0.3155,0.2009,0.2822,0.07 526
852552,M,16.65,21.38,110,904.6,0.1121,0.1457,0.1525,0.0917,0.1995,0.06 33,0.8068,0.9017,5.455,102.6,0.006048,0.01882,0.02741,0.0113,0.01468,0 .002801,26.46,31.56,177,2215,0.1805,0.3578,0.4695,0.2095,0.3613,0.0956 4
852631,M,17.14,16.4,116,912.7,0.1186,0.2276,0.2229,0.1401,0.304,0.0741 3,1.046,0.976,7.276,111.4,0.008029,0.03799,0.03732,0.02397,0.02308,0.0 07444,22.25,21.4,152.4,1461,0.1545,0.3949,0.3853,0.255,0.4066,0.1059 852763,M,14.58,21.53,97.41,644.8,0.1054,0.1868,0.1425,0.08783,0.2252,0 .06924,0.2545,0.9832,2.11,21.05,0.004452,0.03055,0.02681,0.01352,0.014 54,0.003711,17.62,33.21,122.4,896.9,0.1525,0.6643,0.5539,0.2701,0.4264, 0.1275
852781,M,18.61,20.25,122.1,1094,0.0944,0.1066,0.149,0.07731,0.1697,0.0 5699,0.8529,1.849,5.632,93.54,0.01075,0.02722,0.05081,0.01911,0.02293, 0.004217,21.31,27.26,139.9,1403,0.1338,0.2117,0.3446,0.149,0.2341,0.07
421
852973,M,15.3,25.27,102.4,732.4,0.1082,0.1697,0.1683,0.08751,0.1926,0. 0654,0.439,1.012,3.498,43.5,0.005233,0.03057,0.03576,0.01083,0.01768,0 .002967,20.27,36.71,149.3,1269,0.1641,0.611,0.6335,0.2024,0.4027,0.098 76
853201,M,17.57,15.05,115,955.1,0.09847,0.1157,0.09875,0.07953,0.1739,0 .06149,0.6003,0.8225,4.655,61.1,0.005627,0.03033,0.03407,0.01354,0.019 25,0.003742,20.01,19.52,134.9,1227,0.1255,0.2812,0.2489,0.1456,0.2756, 0.07919
853401,M,18.63,25.11,124.8,1088,0.1064,0.1887,0.2319,0.1244,0.2183,0.0 6197,0.8307,1.466,5.574,105,0.006248,0.03374,0.05196,0.01158,0.02007, 0.00456,23.15,34.01,160.5,1670,0.1491,0.4257,0.6133,0.1848,0.3444,0.09 782
853612,M,11.84,18.7,77.93,440.6,0.1109,0.1516,0.1218,0.05182,0.2301,0. 07799,0.4825,1.03,3.475,41,0.005551,0.03414,0.04205,0.01044,0.02273,0. 005667,16.82,28.12,119.4,888.7,0.1637,0.5775,0.6956,0.1546,0.4761,0.14 02
85382601,M,17.02,23.98,112.8,899.3,0.1197,0.1496,0.2417,0.1203,0.2248, 0.06382,0.6009,1.398,3.999,67.78,0.008268,0.03082,0.05042,0.01112,0.02 102,0.003854,20.88,32.09,136.1,1344,0.1634,0.3559,0.5588,0.1847,0.353, 0.08482
854002,M,19.27,26.47,127.9,1162,0.09401,0.1719,0.1657,0.07593,0.1853,0 .06261,0.5558,0.6062,3.528,68.17,0.005015,0.03318,0.03497,0.009643,0.0 1543,0.003896,24.15,30.9,161.4,1813,0.1509,0.659,0.6091,0.1785,0.3672, 0.1123
854039,M,16.13,17.88,107,807.2,0.104,0.1559,0.1354,0.07752,0.1998,0.06 515,0.334,0.6857,2.183,35.03,0.004185,0.02868,0.02664,0.009067,0.0170 3,0.003817,20.21,27.26,132.7,1261,0.1446,0.5804,0.5274,0.1864,0.427,0.1 233
854253,M,16.74,21.59,110.1,869.5,0.0961,0.1336,0.1348,0.06018,0.1896,0 .05656,0.4615,0.9197,3.008,45.19,0.005776,0.02499,0.03695,0.01195,0.02 789,0.002665,20.01,29.02,133.5,1229,0.1563,0.3835,0.5409,0.1813,0.4863 ,0.08633
854268,M,14.25,21.72,93.63,633,0.09823,0.1098,0.1319,0.05598,0.1885,0. 06125,0.286,1.019,2.657,24.91,0.005878,0.02995,0.04815,0.01161,0.0202 8,0.004022,15.89,30.36
点此下载完整数据集
统计学术语中英文对照
统计学术语中英文对照Absolute deviation 绝对离差 Absolute number 绝对数 Absolute residuals 绝对残差 Acceleration array 加速度立体阵 Acceleration in an arbitrary direction 任意方向上的加速度Acceleration normal 法向加速度 Acceleration space dimension 加速度空间的维数 Acceleration tangential 切向加速度 Acceleration vector 加速度向量 Acceptable hypothesis 可接受假设 Accumulation 累积 Accuracy 准确度 Actual frequency 实际频数 Adaptive estimator 自适应估计量 Addition 相加
Addition theorem 加法定理 Additivity 可加性 Adjusted rate 调整率 Adjusted value 校正值 Admissible error 容许误差 Aggregation 聚集性 Alternative hypothesis 备择假设 Among groups 组间 Amounts 总量 Analysis of correlation 相关分析Analysis of covariance 协方差分析Analysis of regression 回归分析Analysis of time series 时间序列分析Analysis of variance 方差分析 Angular transformation 角转换 ANOVA （analysis of variance）方差分析
机械专业中英文对照(完整版)1
机械专业英语词汇 陶瓷ceramics 合成纤维synthetic fibre 电化学腐蚀electrochemical corrosion 车架automotive chassis 悬架suspension 转向器redirector 变速器speed changer 板料冲压sheet metal parts 孔加工spot facing machining 车间workshop 工程技术人员engineer 气动夹紧pneuma lock 数学模型mathematical model 画法几何descriptive geometry 机械制图Mechanical drawing 投影projection 视图view 剖视图profile chart 标准件standard component 零件图part drawing 装配图assembly drawing 尺寸标注size marking 技术要求technical requirements 刚度rigidity 内力internal force 位移displacement 截面section 疲劳极限fatigue limit 断裂fracture 塑性变形plastic distortion 脆性材料brittleness material 刚度准则rigidity criterion 垫圈washer 垫片spacer 直齿圆柱齿轮straight toothed spur gear 斜齿圆柱齿轮helical-spur gear 直齿锥齿轮straight bevel gear 运动简图kinematic sketch 齿轮齿条pinion and rack 蜗杆蜗轮worm and worm gear 虚约束passive constraint 曲柄crank 摇杆racker 凸轮cams 共轭曲线conjugate curve 范成法generation method 定义域definitional domain 值域range 导数\\微分differential coefficient 求导derivation 定积分definite integral 不定积分indefinite integral 曲率curvature 偏微分partial differential 毛坯rough 游标卡尺slide caliper 千分尺micrometer calipers 攻丝tap 二阶行列式second order determinant 逆矩阵inverse matrix 线性方程组linear equations 概率probability 随机变量random variable 排列组合permutation and combination 气体状态方程equation of state of gas 动能kinetic energy 势能potential energy 机械能守恒conservation of mechanical energy 动量momentum 桁架truss 轴线axes 余子式cofactor 逻辑电路logic circuit 触发器flip-flop 脉冲波形pulse shape 数模digital analogy 液压传动机构fluid drive mechanism 机械零件mechanical parts 淬火冷却quench 淬火hardening 回火tempering 调质hardening and tempering 磨粒abrasive grain 结合剂bonding agent 砂轮grinding wheel 后角clearance angle 龙门刨削planing 主轴spindle
数据挖掘考试题目聚类
数据挖掘考试题目——聚类 一、填空题 1、密度的基于中心的方法使得我们可以将点分类为：__________、________ 、_________。 2、DBSCAN算法在最坏的情况下，时间复杂度是__________、空间复杂度是__________。 3、DBSCAN算法的优点是_______、__________________________。 4、DBSCAN算法的缺点是处理_________________、_____________的数据效果不好。 5、DBSCAN算法的参数有：___________、____________。 6、簇的有效性的非监督度量常常可以分为两类：__________、__________，它常采用的指标为__________。 7、簇的有效性的监督度量通常称为___________，它度量簇标号与外部提供的标号的匹配程度主要借助____________。 8、在相似度矩阵评价的聚类中，如果有明显分离的簇，则相似度矩阵应当粗略地是__________。 9、DBSCAN算法的参数确定的基本方法是观察____________________的特性。 10、不引用附加的信息，评估聚类分析结果对数据拟合情况属于__________技术。 答案： 1、核心点边界点噪声点 2、O(n2) O(n) 3、耐噪声能够处理任意大小和形状的簇 4、高维数据变密度的 5、EPS MinPts 6、簇的凝聚性簇的分离性均方差(SSE) 7、外部指标监督指标的熵 8、块对角的 9、点到它的第K个最近邻的距离（K-距离） 10、非监督 二、选择题 1、DBSCAN算法的过程是（B）。 ①删除噪声点。 ②每组连通的核心点形成一个簇。 ③将所有点标记为核心点、边界点和噪声点。 ④将每个边界点指派到一个与之关联的核心点的簇中。 ⑤为距离在Eps之内的所有核心点之间赋予一条边。 A：①②④⑤③ B：③①⑤②④ C：③①②④⑤ D：①④⑤②③ 2、如果有m个点，DBSCAN在最坏的情况下的时间复杂度度为（C）。 A O(m) B O(mlogm) C O(m2) D O(logm) 3、在基本DBSCAN的参数选择方法中，点到它的K个最近邻的距离中的K选作为哪一个参数（B）。 A Eps B MinPts C 质心 D 边界
常用机械制图手工绘图工具及使用技巧
常用机械制图手工绘图工具及使用 技巧 熟练掌握常用的绘图工具使用技巧,对于提高手工绘图的质量和速率有重要意义。 —、常用绘图工具 （1）（图板）画图时，需将图纸平铺在图纸上，所以，图板的表面必须平整、光洁、且富有弹性。图板 的左侧边称为导边，必须平直。常用的图板规格有0号、1号和二号三种。 （2）丁字尺丁字尺主要用于画水平线，它由尺头和尺身组成。尺头和尺身的连接处必须牢固，尺头的 内侧边与尺身的上边（称为工作边）必须垂直。使用时，用左手扶住尺头，将尺头的内侧边紧贴图板的 导边，上下移动丁字尺，自左向右可画出一系列不同位置的水平线，如图1–18a所示。 （3）三角板三角板有45°-90°角和30°-60°-90°角的各一块。将一块三角板与丁字尺配合使用，自下而上 可画出一系列不同位置的直线，如图1-18b所示；还可画与水平线成特殊角度如30°、45°、60°的倾斜线，如图1-18c所示将两快三角板与丁字尺配合使用，可画出与水平线成15°、75°的倾斜线，如图2所示。两块三角板互相配合使用，可任画已知直线的水平线或垂直线，如图3所示。 图1用丁字尺和三角板画线 图2画15度75度斜线 图3画已知直线平行线和垂直线 二、分规、比例尺 (1)分规分规是用来量取尺寸、截取线段、等分线段的工具。分规的两腿端部有钢针，当两腿合龙时， 两针尖应重合于一点，如图4所示。图5所示为用分规在比例尺上量取尺寸（图5a），然后在线上连续截取等长线段（图5b）的方法若欲将图5c所示的AB线段四等分，可先任凭自测估计，将分规的两针 尖开到约为AB/4进行试分，如有剩余（或不足）时，再将针尖间的距离张大（或缩小）e/4，e为剩余或不足量，再进行试分，直到满意为止。用试分法也可等分圆或圆弧。 (2)比例尺比例尺的三个棱面上有六种不同比例的刻度，如1：100、1：200等，可用于量取不同比例的 尺寸。 图5分规画法 三、圆规圆规是用来画圆或圆弧的工具。圆规固定腿上的钢针具有两种不同形状的尖端：带台阶的尖端是画圆货圆弧时定心用的；带锥形的尖端可作分规使用。活动腿上有肘形关节，可随时装换铅芯插脚、 鸭嘴脚及作分规用的锥形钢针插脚，如图6所示。 图6圆规及附件 画圆或圆弧时，要注意调整钢针在固定腿上的位置，使两腿在合龙时针尖比铅芯稍长些，以便将针尖全部扎入内，如图7a所示；按顺时针方向转动圆规，并稍向前倾斜，此时，要保证针尖和笔尖均垂直纸 面，如图7b所示；画大圆时，可接上延长杆后使用，如图7c所示。 图7圆规用法 四、曲线板曲线板是绘制非圆曲线的常用工具。画线时，先徒手将各点轻轻地连成曲线，如图8a所示；然后在曲线板上选取曲率相当的部分，分几段逐次将各点连成曲线，但每段都不要全部描完，至少留出后两点间的一小段，使之与下段吻合，以保证曲线的光滑连接，如图8b所示。 图8非圆曲线的描绘 五、铅笔(1)铅笔的型号及应用绘图铅笔分软与硬两种型号，字母“B”表示软铅笔，字母“H”表示硬铅芯。“B” 之前的数值越大，表示铅芯越硬。 之前的数值越大，表示铅芯越软；“H” 字母“HB”表示软硬适中的铅芯。 图9修磨铅笔的方法
统计学名词中英文对照三
统计学名词中英文对照三
统计学名词中英文对照三 统计学名词中英文对照A abscissa 横坐标 absence rate 缺勤率 absolute number 绝对数 absolute value 绝对值 accident error 偶然误差 accumulated frequency 累积频数 alternative hyothesis 备择假设 analysis of data 分析资料 analysis of variance(ANOVA) 方差分析 arith-log aer 算术对数纸 arithmetic mean 算术均数 assumed mean 假定均数 arithmetic weighted mean 加权算术均数asymmetry coefficient 偏度系数
average 平均数 average deviation 平均差 B bar chart 直条图、条图 bias 偏性 binomial distribution 二项分布 biometrics 生物统计学 bivariate normal oulation 双变量正态总体 C cartogram 统计图 case fatality rate(or case mortality) 病死率census 普查 chi-sguare(X2) test 卡方检验 central tendency 集中趋势 class interval 组距 classification 分组、分类 cluster samling 整群抽样 coefficient of correlation 相关系数
《数据挖掘》试题与标准答案
一、解答题（满分3０分，每小题5分) 1. 怎样理解数据挖掘和知识发现的关系?请详细阐述之 首先从数据源中抽取感兴趣的数据，并把它组织成适合挖掘的数据组织形式;然后,调用相应的算法生成所需的知识;最后对生成的知识模式进行评估，并把有价值的知识集成到企业的智能系统中。 知识发现是一个指出数据中有效、崭新、潜在的、有价值的、一个不可忽视的流程，其最终目标是掌握数据的模式。流程步骤:先理解要应用的领域、熟悉相关知识，接着建立目标数据集，并专注所选择的数据子集;再作数据预处理，剔除错误或不一致的数据；然后进行数据简化与转换工作；再通过数据挖掘的技术程序成为模式、做回归分析或找出分类模型；最后经过解释和评价成为有用的信息。 2.时间序列数据挖掘的方法有哪些，请详细阐述之 时间序列数据挖掘的方法有: １)、确定性时间序列预测方法:对于平稳变化特征的时间序列来说，假设未来行为与现在的行为有关，利用属性现在的值预测将来的值是可行的。例如，要预测下周某种商品的销售额，可以用最近一段时间的实际销售量来建立预测模型。 ２）、随机时间序列预测方法：通过建立随机模型,对随机时间序列进行分析,可以预测未来值。若时间序列是平稳的,可以用自回归(Auto Ｒegressiｖe，简称AR)模型、移动回归模型(Ｍovｉng Ａveｒage,简称ＭＡ）或自回归移动平均(Aｕto Regrｅssive Moｖiｎg Aｖeraｇe，简称AＲMＡ)模型进行分析预测。 3)、其他方法:可用于时间序列预测的方法很多,其中比较成功的是神经网络。由于大量的时间序列是非平稳的,因此特征参数和数据分布随着时间的推移而变化。假如通过对某段历史数据的训练，通过数学统计模型估计神经网络的各层权重参数初值，就可能建立神经网络预测模型,用于时间序列的预测。
统计学词汇中英文对照完整版
统计学词汇中英文对照完整版 Absolute deviation, 绝对离差 Absolute number, 绝对数 Absolute residuals, 绝对残差 Acceleration array, 加速度立体阵 Acceleration in an arbitrary direction, 任意方向上的加速度Acceleration normal, 法向加速度 Acceleration space dimension, 加速度空间的维数Acceleration tangential, 切向加速度 Acceleration vector, 加速度向量 Acceptable hypothesis, 可接受假设 Accumulation, 累积 Accuracy, 准确度 Actual frequency, 实际频数 Adaptive estimator, 自适应估计量 Addition, 相加 Addition theorem, 加法定理 Additive Noise, 加性噪声 Additivity, 可加性 Adjusted rate, 调整率 Adjusted value, 校正值 Admissible error, 容许误差 Aggregation, 聚集性 Alpha factoring,α因子法 Alternative hypothesis, 备择假设 Among groups, 组间 Amounts, 总量 Analysis of correlation, 相关分析 Analysis of covariance, 协方差分析 Analysis Of Effects, 效应分析 Analysis Of Variance, 方差分析 Analysis of regression, 回归分析 Analysis of time series, 时间序列分析 Analysis of variance, 方差分析 Angular transformation, 角转换 ANOVA （analysis of variance）, 方差分析 ANOVA Models, 方差分析模型 ANOVA table and eta, 分组计算方差分析 Arcing, 弧/弧旋 Arcsine transformation, 反正弦变换 Area 区域图 Area under the curve, 曲线面积 AREG , 评估从一个时间点到下一个时间点回归相关时的误差
电机零部件中英文对照表
串激电机：universal motor 转子： 转轴：shaft 换向器：commutator 铁芯：rotor steel core 端板：end spider 绝缘纸：rotor steel liner 槽楔（环）：wedge ring U型挡圈：U type retainer 漆包线：field magnet wire 轴承：bearing 绝缘漆：varnish 换向片：commutator segment 定子： 铁芯：rotor steel core 漆包线：field magnet wire 绝缘纸：rotor steel liner 支架：bracket 支撑组件： 机壳：housing 端盖：support frame 碳刷组件： 碳刷：brush 碳刷套：brush sleeve 碳刷架(刷握)：brush holder 弹簧：brush spring 刷辫：brush flexible 刷盒：brush box 风机部分： 定叶轮：diffuser（guide vane）动叶轮：impeller 风罩：fan cover 螺钉：screw 螺帽：nut-fan 压圈：gasket-fan 衬套：bush-fan 爬电距离：creepage distances specify ：指定（动） 附加绝缘：supplementary insulation clause ：条款（名） 基本绝缘：basic insulation wear ：磨损（动） 加强绝缘：reinforced insulation clearance ：电气间隙 Clearance and creepage distances over supplementary and reinforced insulation not reduced below the values specified in clause 29 as a result of wear. accessible ：易接近的，可到达的，易受影响的 loose ：宽松的，不牢固的 Creepage distances and clearance between live parts and accessible parts not reduced below values specified for supplementary insulation if wires ,screws etc. becomes loose. Vaccum吸尘器Hose：软管Attachment ：附件，附加装置Transformer ：变压器 From the result of our inspection and tests on the submitted samples, we conclude that that they comply with the requirements of the standards. Submit:提交，递交inspection : 检查comply with : 符合 All models have same constructions, and are portable,dry-pick up vaccum cleaners for household use .Different models in one series with different postfixes are same products, but marked with different powers. Refer to below table for difference between models in different series. Postfix : 后缀refer to : 查阅提到谈到打听
(完整word版)各种聚类算法介绍及对比
一、层次聚类 1、层次聚类的原理及分类 1）层次法（Hierarchical methods）先计算样本之间的距离。每次将距离最近的点合并到同一个类。然后，再计算类与类之间的距离，将距离最近的类合并为一个大类。不停的合并，直到合成了一个类。其中类与类的距离的计算方法有：最短距离法，最长距离法，中间距离法，类平均法等。比如最短距离法，将类与类的距离定义为类与类之间样本的最短距离。 层次聚类算法根据层次分解的顺序分为：自下底向上和自上向下，即凝聚的层次聚类算法和分裂的层次聚类算法（agglomerative和divisive），也可以理解为自下而上法（bottom-up）和自上而下法（top-down）。自下而上法就是一开始每个个体（object）都是一个 类，然后根据linkage寻找同类，最后形成一个“类”。自上而下法就是反过来，一开始所有个体都属于一个“类”，然后根据linkage排除异己，最后每个个体都成为一个“类”。这两种路方法没有孰优孰劣之分，只是在实际应用的时候要根据数据特点以及你想要的“类”的个数，来考虑是自上而下更快还是自下而上更快。至于根据Linkage判断“类” 的方法就是最短距离法、最长距离法、中间距离法、类平均法等等（其中类平均法往往被认为是最常用也最好用的方法，一方面因为其良好的单调性，另一方面因为其空间扩张/浓缩的程度适中）。为弥补分解与合并的不足，层次合并经常要与其它聚类方法相结合，如循环定位。 2）Hierarchical methods中比较新的算法有BIRCH（Balanced Iterative Reducing and Clustering Using Hierarchies利用层次方法的平衡迭代规约和聚类）主要是在数据量很大的时候使用，而且数据类型是numerical。首先利用树的结构对对象集进行划分，然后再利用其它聚类方法对这些聚类进行优化；ROCK（A Hierarchical Clustering Algorithm for Categorical Attributes）主要用在categorical的数据类型上；Chameleon（A Hierarchical Clustering Algorithm Using Dynamic Modeling）里用到的linkage是kNN（k-nearest-neighbor）算法，并以此构建一个graph，Chameleon的聚类效果被认为非常强大，比BIRCH好用，但运算复杂度很高，O(n^2)。 2、层次聚类的流程 凝聚型层次聚类的策略是先将每个对象作为一个簇，然后合并这些原子簇为越来越大的簇，直到所有对象都在一个簇中，或者某个终结条件被满足。绝大多数层次聚类属于凝聚型层次聚类，它们只是在簇间相似度的定义上有所不同。这里给出采用最小距离的凝聚层次聚类算法流程： (1) 将每个对象看作一类，计算两两之间的最小距离； (2) 将距离最小的两个类合并成一个新类； (3) 重新计算新类与所有类之间的距离； (4) 重复(2)、(3)，直到所有类最后合并成一类。
统计学名词中英文对照上课讲义
统计学名词中英文对 照
统计学名词中英文对照默认分类 A abscissa 横坐标 absence rate 缺勤率 absolute number 绝对数absolute value 绝对值 accident error 偶然误差accumulated frequency 累积频数alternative hypothesis 备择假设analysis of data 分析资料analysis of variance(ANOVA) 方差分析 arith-log paper 算术对数纸arithmetic mean 算术均数assumed mean 假定均数arithmetic weighted mean 加权算术均数 asymmetry coefficient 偏度系数average 平均数 average deviation 平均差 B bar chart 直条图、条图 bias 偏性 binomial distribution 二项分布 biometrics 生物统计学 bivariate normal population 双变 量正态总体 C cartogram 统计图 case fatality rate(or case mortality) 病死率 census 普查 chi-sguare(X2) test 卡方检验 central tendency 集中趋势 class interval 组距 classification 分组、分类 cluster sampling 整群抽样 coefficient of correlation 相关系 数 coefficient of regression 回归系 数 coefficient of variability(or coefficieut of variation) 变异系数 collection of data 收集资料 column 列（栏） combinative table 组合表 combined standard deviation 合 并标准差 combined variance(or poolled variance) 合并方差 complete survey 全面调查 completely correlation 完全相关 completely random design 完全 随机设计 confidence interval 可信区间， 置信区间 confidence level 可信水平，置 信水平 confidence limit 可信限，置信限 constituent ratio 构成比，结构相 对数 continuity 连续性 control 对照 control group 对照组 coordinate 坐标 correction for continuity 连续性 校正 correction for grouping 归组校正 correction number 校正数 收集于网络，如有侵权请联系管理员删除
cnc数控机床中英文对照表
cnc数控机床中英文对照表 来源：网络转载 3-Jaws indexing spacers 三爪、分割工具头 A.T.C.system 加工中心机刀库 Aluminum continuous melting & holding furnaces 连续溶解保温炉Balancing equipment 平衡设备 Bayonet 卡口 Bearing fittings 轴承配件 Bearing processing equipment 轴承加工机 Bearings 轴承 Belt drive 带传动 Bending machines 弯曲机 Blades 刀片 Blades,saw 锯片 Bolts,screws & nuts 螺栓,螺帽及螺丝 Boring heads 搪孔头 Boring machines 镗床 Cable making tools 造线机 Casting,aluminium 铸铝 Casting,copper 铸铜 Casting,gray iron 铸灰口铁 Casting,malleable iron 可锻铸铁 Casting,other 其他铸造 Casting,steel 铸钢 Chain drive 链传动 Chain making tools 造链机 Chamfer machines 倒角机 Chucks 夹盘 Clamping/holding systems 夹具/支持系统 CNC bending presses 电脑数控弯折机 CNC boring machines 电脑数控镗床 CNC drilling machines 电脑数控钻床 CNC EDM wire-cutting machines 电脑数控电火花线切削机 CNC electric discharge machines 电脑数控电火花机 CNC engraving machines 电脑数控雕刻机 CNC grinding machines 电脑数控磨床 CNC lathes 电脑数控车床 CNC machine tool fittings 电脑数控机床配件 CNC milling machines 电脑数控铣床 CNC shearing machines 电脑数控剪切机 CNC toolings CNC刀杆 CNC wire-cutting machines 电脑数控线切削机
各种聚类算法的比较
各种聚类算法的比较 聚类的目标是使同一类对象的相似度尽可能地小；不同类对象之间的相似度尽可能地大。目前聚类的方法很多，根据基本思想的不同，大致可以将聚类算法分为五大类：层次聚类算法、分割聚类算法、基于约束的聚类算法、机器学习中的聚类算法和用于高维度的聚类算法。摘自数据挖掘中的聚类分析研究综述这篇论文。 1、层次聚类算法 1.1聚合聚类 1.1.1相似度依据距离不同：Single-Link:最近距离、Complete-Link：最远距离、Average-Link：平均距离 1.1.2最具代表性算法 1）CURE算法 特点：固定数目有代表性的点共同代表类 优点：识别形状复杂，大小不一的聚类，过滤孤立点 2）ROCK算法 特点：对CURE算法的改进 优点：同上，并适用于类别属性的数据 3）CHAMELEON算法 特点：利用了动态建模技术 1.2分解聚类 1.3优缺点 优点：适用于任意形状和任意属性的数据集；灵活控制不同层次的聚类粒度，强聚类能力 缺点：大大延长了算法的执行时间，不能回溯处理 2、分割聚类算法 2.1基于密度的聚类 2.1.1特点 将密度足够大的相邻区域连接，能有效处理异常数据，主要用于对空间数据的聚类
1）DBSCAN：不断生长足够高密度的区域 2）DENCLUE：根据数据点在属性空间中的密度进行聚类，密度和网格与处理的结合 3）OPTICS、DBCLASD、CURD：均针对数据在空间中呈现的不同密度分不对DBSCAN作了改进 2.2基于网格的聚类 2.2.1特点 利用属性空间的多维网格数据结构，将空间划分为有限数目的单元以构成网格结构； 1）优点：处理时间与数据对象的数目无关，与数据的输入顺序无关，可以处理任意类型的数据 2）缺点：处理时间与每维空间所划分的单元数相关，一定程度上降低了聚类的质量和准确性 2.2.2典型算法 1）STING：基于网格多分辨率，将空间划分为方形单元，对应不同分辨率2）STING+：改进STING，用于处理动态进化的空间数据 3）ＣＬＩＱＵＥ：结合网格和密度聚类的思想，能处理大规模高维度数据4）WaveCluster：以信号处理思想为基础 2.3基于图论的聚类 2.3.1特点 转换为组合优化问题，并利用图论和相关启发式算法来解决，构造数据集的最小生成数，再逐步删除最长边 1）优点：不需要进行相似度的计算 2.3.2两个主要的应用形式 1）基于超图的划分 2）基于光谱的图划分 2.4基于平方误差的迭代重分配聚类 2.4.1思想 逐步对聚类结果进行优化、不断将目标数据集向各个聚类中心进行重新分配以获最优解
统计英文词汇
A abscissa横坐标 absence rate缺勤率 absolute number绝对数 absolute value绝对值 accident error偶然误差 accumulated frequency累积频数 alternative hypothesis备择假设 analysis of data分析资料 analysis of variance(ANOVA)方差分析 arith-log paper算术对数纸 arithmetic mean算术均数 assumed mean假定均数 arithmetic weighted mean加权算术均数asymmetry coefficient偏度系数 average平均数 average deviation平均差 B bar chart直条图、条图 bias偏性 binomial distribution二项分布 biometrics生物统计学 bivariate normal population双变量正态总体 C cartogram统计图 case fatality rate(or case mortality)病死率 census普查 chi-sguare(X2) test卡方检验 central tendency集中趋势 class interval组距 classification分组、分类 cluster sampling整群抽样 coefficient of correlation相关系数 coefficient of regression回归系数 coefficient of variability(or coefficieut of variation)变异系数 collection of data收集资料 column列（栏） combinative table组合表 combined standard deviation合并标准差 combined variance(or poolled variance)合并方差complete survey全面调查
统计学中英文对照表
统计学中英文对照表
统计学中英文对照表 2008-03-21 11:39 Absolutedeviation,绝对离差 Absolutenumber,绝对数 Absoluteresiduals,绝对残差 Accelerationarray,加速度立体阵Accelerationinanarbitrarydirection,任意方向上的加速度Accelerationnormal,法向加速度Accelerationspacedimension,加速度空间的维数Accelerationtangential,切向加速度Accelerationvector,加速度向量Acceptablehypothesis,可接受假设 Accumulation,累积 Accuracy,准确度 Actualfrequency,实际频数 Adaptiveestimator,自适应估计量 Addition,相加 Additiontheorem,加法定理 Additivity,可加性 Adjustedrate,调整率 Adjustedvalue,校正值 Admissibleerror,容许误差 Aggregation,聚集性 Alternativehypothesis,备择假设 Amonggroups,组间 Amounts,总量 Analysisofcorrelation,相关分析Analysisofcovariance,协方差分析Analysisofregression,回归分析Analysisoftimeseries,时间序列分析Analysisofvariance,方差分析Angulartransformation,角转换 ANOVA（analysisofvariance）,方差分析ANOVAModels,方差分析模型 Arcing,弧/弧旋 Arcsinetransformation,反正弦变换Areaunderthecurve,曲线面积
常用机械零件中英文对照
常用机械零件中英文对照 螺母NUT 垫片/垫圈WASHER 调整螺母ADJUSTING NUT 弹簧SPRING 阀瓣VALVE DISC 密封圈SEAL RING 阀体VALVE BODY 镀镍NICKEL PLATING 20#钢表面镀黄锌YELLOW ZINC PLATING 外螺纹MALE THREAD 内螺纹FEMALE THREAD 性能规范PERFORMANCE SPECIFICATION 公称通径NOMINAL DIAMETER 公称压力NOMINAL PRESSURE 强度试验STRENGTH TEST 额定排量CERTIFIED DISCHARGE CAPACITY 适用介质APPLICABLE MEDIA 制冷剂，润滑油和水REFRIGERANT，LUBRICANT AND WATER 螺栓BOLTS 螺丝钉SCREW 垫圈WASHER 金属垫片GASKET指软质橡胶垫圈等 接头COUPLING 闷头PLUG 弯头ELBOW 封头HEAD 法兰FLANGE 变径接头REDUCER 支座SADDLE 三通TEE 换热管HEAT EXCHANGE TUBE 铭牌NAMEPLATE 铭牌座N/P BRACKET 轴流风扇AXIAL FAN 离心风机CENTRIFUGAL FAN 电机MOTOR 水泵PUMP
压差阀PRESSURE DIFFERENTIAL VALVE 背压阀COUNTERBALANCE VALVE 温度控制阀 TEMPERATURE CONTROL VALVE 液位控制阀 LEVEL CONTROL VALVE 手动膨胀阀 HAND EXPANSION VALVE 热力膨胀阀 THERMOSTATIC EXPANSION VALVE 四通阀FOUR WAY VALVE 电磁阀SOLENOID VALVE 截止阀GLOBE VALVE 角阀ANGLE VALVE 直通阀STRAIGHT VALVE 球阀BALL VALVE 蝶阀BUTTERFLY VALVE 换向阀REVERSING VALVE 止回阀CHECK VALVE 吸气止回阀 SUCTION CHECK VALVE 分流头DISTRIBUTOR 干燥过滤器 DRY FILTER 视液镜MOISTURE LIQUID GLASS 压力开关PRESSURE SWITCH 储液罐LIQUID RECEIVER 油分离器OIL SEPARATOR 油过滤器OIL FILTER 油泵OIL PUMP 液位计LEVEL GAUGE 温度计THERMOMETER 压力表MANOMETER 液位开关LEVEL SWITCH 管夹及卡箍 CLAMPS & HOOPS 电气类元件 ELECTRIC COMPONENTS 传感器SENSORS 插座RECEPTACLE 接头CONNECTER 控制箱CONTROL PANEL 性能指标. PERFORMANCE INDEX 标志面MARK SURFACE 三通接头TEE CONNECTION 三通接头体 TEE CONNECTION BODY 扁螺母FLAT NUT 备注REMARK 下压板LOWER PLATE
数据挖掘实验报告-聚类分析
数据挖掘实验报告（三） 聚类分析 姓名：李圣杰 班级：计算机1304 学号：1311610602
一、实验目的 1、掌握k-means 聚类方法； 2、通过自行编程，对三维空间内的点用k-means 方法聚类。 二、实验设备 PC 一台，dev-c++5.11 三、实验内容 1.问题描述： 立体空间三维点的聚类. 说明：数据放在数据文件中(不得放在程序中)，第一行是数据的个数，以后各行是各个点的x,y,z 坐标。 2.设计要求 读取文本文件数据，并用K-means 方法输出聚类中心 3. 需求分析 k-means 算法接受输入量k ；然后将n 个数据对象划分为 k 个聚类以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。聚类相似度是利用各聚类中对象的均值所获得一个“中心对象”（引力中心）来进行计算的。 k-means 算法的工作过程说明如下：首先从n 个数据对象任意选择k 个对象作为初始聚类中心，而对于所剩下的其它对象，则根据它们与这些聚类中心的相似度（距离），分别将它们分配给与其最相似的（聚类中心所代表的）聚类。然后，再计算每个所获新聚类的聚类中心（该聚类中所有对象的均值），不断重复这一过程直到标准测度函数开始收敛为止。一般都采用均方差作为标准测度函数，具体定义如下： 2 1∑∑=∈-=k i i i E C p m p (1) 其中E 为数据库中所有对象的均方差之和，p 为代表对象的空间中的一个点，m i 为聚类C i 的均值(p 和m i 均是多维的)。公式(1)所示的聚类标准，旨在使所获得的k 个聚类具有以下特点：各聚类本身尽可能的紧凑，而各聚类之间尽可能的分开。 四、实验步骤 Step 1.读取数据组，从N 个数据对象任意选择k 个对象作为初始聚类中心； Step 2.循环Step 3到Step 4直到每个聚类不再发生变化为止； Step 3.根据每个聚类对象的均值（中心对象），计算每个对象与这些中心对象的距离，并根据最小距离重新对相应对象进行划分； Step 4.重新计算每个（有变化）聚类的均值（中心对象）。 代码 #include
常用机械部件与工具的认识和使用
实训1 常用机械部件与工具的认识和使用 知识引导： 随着社会信息化的加速，图文信息设备大量地应用于办公自动化领域。其设备可分为计算机类（各种类型计算机、计算机网络系统、图文处理设备、电子会议设备等）、通信设备类（电话、传真机、局域网、程控交换机等）和办公机械类（复印机、打印机、绘图仪、扫描仪、投影机、桌面轻印刷系统、碎纸机、装订机等）。在各类设备的日常使用、保养与维护中，拆解和维护维修设备时经常要用到各种工具，常用的拆解维护工具有：螺丝刀、镊子、排刷、吹气球、脱脂棉等，常用的维护维修工具有：各式钳子、扳手、万用表、电烙铁等。另外，各类图文信息设备上都离不开各种连接和固定用的螺丝、卡簧等机械部件。 实训仪器及耗材： 工具箱，机械部件，如各类螺丝、螺栓和螺母、平垫片等。 实训目的： 本实训目的在于认识各种螺丝、卡簧等机械部件，认识拆解和维护维修设备常用的工具，熟悉工具的作用和正确的使用方法，并练习各类工具的使用。 实训要求： 1、认识螺丝、卡簧等机械部件。 2、认识常用工具的形状、名称和基本用途。 3、知道常用工具的正确使用方法。 4、练习使用常用的各类工具。
实训步骤： 1、通过讲解和展示认识工具箱中的各类工具和常用机械部件。 2、通过讲解和演示操作对工具的作用和正确使用方法进行了解和掌握。 3、将各类机械零件和工具的用途填写在表格中。 4、动手练习使用常用工具，如：螺丝刀、尖嘴钳等。 机械部件与工具展示： 螺丝 在图文信息设备上，螺丝用的非常多。螺丝有很多种类，要根据它的用途的不同，选择对应的使用方法。一般的，小的叫做螺丝，大的叫做螺栓。从螺丝的正上方看到的头部形状分为两种：一字螺丝（－）和十字螺丝（＋）。如图1-1所示。 图1-1 一字螺丝和十字螺丝 小螺丝 直径在1mm－8mm的小型螺丝叫做小螺丝。表面涂黑的小螺丝叫“黑螺丝”，表面镀银色的的小螺丝叫“化妆螺丝”。小螺丝用于不需要太费力的部件的安装等。 黑螺丝主 图1-2 小螺丝