K近邻应用-异常检测应用
原理:
根据数据样本进行KMeans机器学习模型的建立,获取簇心点,以簇为单位,离簇心最远的第五个点的距离为阈值,大于这个值的为异常点,即获得数据异常。
如图:
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" alt="" />
数据样本:
1,2.43,2.3899999 2,2.38,2.12 3,2.8,2.51 4,2.01,2.69 5,2.71,2.45 6,2.55,2.34
7,2.46,2.31 8,2.27,2.38 9,2.87,2.55 10,2.75,2.07 11,2.3899999,2.6100001 12,2.67,2.31
13,2.68,2.75 14,2.47,2.05 15,2.96,2.66 16,2.08,2.92 17,2.58,2.12 18,2.69,2.72
19,2.29,2.81 20,2.2,2.2 21,2.46,2.87 22,2.66,2.92 23,2.71,2.63 24,2.09,2.99
25,2.33,2.84 26,2.4,2.63 27,2.05,2.27 28,2.59,2.81 29,2.68,2.72 30,2.5,2.29
31,2.63,2.8899999 32,2.35,2.8600001 33,2.74,2.06 34,2.83,2.56 35,2.3600001,2.87 36,2.25,2.32
37,2.99,2.85 38,2.19,2.62 39,2.37,2.19 40,2.37,2.08 41,2.62,2.25 42,2.16,2.56
43,2.08,2.37 44,2.77,2.55 45,2.96,2.85 46,2.52,2.24 47,2.6,2.55 48,2.78,2.14
49,2.76,2.42 50,2.05,2.67 51,2.94,2.82 52,2.52,2.59 53,2.04,2.08 54,2.65,2.03
55,2.32,2.88 56,2.96,2.2 57,2.97,2.28 58,2.01,2.6399999 59,2.58,2.52 60,2.55,2.7
61,2.75,2.19 62,2.28,2.48 63,2.6399999,2.54 64,2.34,2.27 65,2.72,2.23 66,2.5,2.35
67,2.25,2.2 68,2.27,2.91 69,2.8899999,2.88 70,2.76,2.48 71,2.63,2.22 72,2.69,2.33
73,2.9,2.02 74,2.23,2.26 75,2.82,2.87 76,2.57,2.83 77,2.97,2.47 78,2.69,2.54
79,2.6,2.84 80,2.98,2.99 81,2.21,2.3899999 82,2.11,2.46 83,2.54,2.77 84,2.57,2.19
85,2.66,2.77 86,2.4,2.88 87,2.43,2.75 88,2.35,2.05 89,2.68,2.25 90,2.43,2.87
91,2.06,2.05 92,2.8600001,2.6100001 93,2.58,2.75 94,2.91,2.8 95,2.38,2.95 96,2.63,2.58
97,2.82,2.93 98,2.72,2.97 99,2.16,2.55 100,5.46,5.1 101,5.9,5.39 102,5.81,5.91
103,5.92,5.65 104,5.91,5.94 105,5.9,5.91 106,5.7799997,5.66 107,5.76,5.32 108,5.11,5.77
109,5.38,5.46 110,5.63,5.76 111,5.1,5.7200003 112,5.66,5.31 113,5.86,5.6 114,5.46,5.74
115,5.76,5.17 116,5.39,5.24 117,5.33,5.49 118,5.05,5.28 119,5.8,5.63 120,5.0,5.18
121,5.35,5.71 122,5.5299997,5.45 123,5.95,5.04 124,5.17,5.32 125,5.83,5.56 126,5.67,5.55
127,5.63,5.25 128,5.42,5.27 129,5.38,5.57 130,5.39,5.6 131,5.88,5.41 132,5.84,5.38
133,5.95,5.36 134,5.65,5.43 135,5.76,5.05 136,5.65,5.5 137,5.13,5.07 138,5.79,5.87
139,5.87,5.38 140,5.9,5.96 141,5.28,5.05 142,5.8,5.61 143,5.24,5.24 144,5.08,5.35
145,5.38,5.5299997 146,5.4,5.62 147,5.73,5.0 148,5.3,5.1 149,5.34,5.39 150,5.63,5.34
151,5.4,5.29 152,5.23,5.26 153,5.04,5.25 154,5.49,5.83 155,5.89,5.18 156,5.18,5.85
157,5.41,5.67 158,5.81,5.7200003 159,5.62,5.41 160,5.79,5.5 161,5.35,5.94 162,5.31,5.68
163,5.14,5.74 164,5.37,5.59 165,5.19,5.91 166,5.62,5.64 167,5.26,5.38 168,5.74,5.91
169,5.17,5.8 170,5.68,5.13 171,5.67,5.21 172,5.2,5.49 173,5.89,5.87 174,5.8,5.22
175,5.01,5.31 176,5.0,5.28 177,5.95,5.56 178,5.27,5.23 179,5.9,5.74 180,5.21,5.75
181,5.13,5.3 182,5.36,5.0 183,5.21,5.86 184,5.21,5.56 185,5.7799997,5.15 186,5.04,5.4
187,5.52,5.61 188,5.02,5.99 189,5.32,5.04 190,5.81,5.51 191,5.76,5.29 192,5.03,5.62
193,5.08,5.26 194,5.42,5.4 195,5.28,5.04 196,5.2,5.49 197,5.7799997,5.33 198,5.38,5.71
199,5.9700003,5.96 200,8.51,8.93 201,8.43,8.58 202,8.62,8.31 203,8.08,8.52 204,8.31,8.49
205,8.4,8.97 206,8.6,8.74 207,8.96,8.76 208,8.0,8.79 209,8.04,8.0 210,8.71,8.23
211,8.78,8.4 212,8.85,8.34 213,8.04,8.74 214,8.92,8.55 215,8.0,8.9 216,8.24,8.45
217,8.33,8.35 218,8.83,8.94 219,8.23,8.06 220,8.46,8.85 221,8.39,8.59 222,8.7,8.85
223,8.45,8.68 224,8.86,8.74 225,8.11,8.18 226,8.11,8.27 227,8.15,8.35 228,8.99,8.27
229,8.67,8.12 230,8.18,8.92 231,8.58,8.58 232,8.05,8.67 233,8.97,8.11 234,8.76,8.49
235,8.18,8.54 236,8.82,8.64 237,8.74,8.89 238,8.82,8.77 239,8.02,8.33 240,8.77,8.54
241,8.22,8.13 242,8.92,8.35 243,8.71,8.55 244,8.12,8.74 245,8.07,8.96 246,8.71,8.17
247,8.12,8.4 248,8.03,8.92 249,8.99,8.55 250,8.63,8.19 251,8.95,8.82 252,8.25,8.32
253,8.08,8.21 254,8.31,8.94 255,8.87,8.3 256,8.72,8.23 257,8.98,8.88 258,8.48,8.64
259,8.81,8.3 260,8.15,8.07 261,8.36,8.02 262,8.16,8.22 263,8.77,8.44 264,8.51,8.17
265,8.28,8.31 266,8.57,8.47 267,8.95,8.1 268,8.91,8.72 269,8.34,8.64 270,8.07,8.99
271,8.3,8.75 272,8.35,8.75 273,8.9,8.22 274,8.99,8.94 275,8.67,8.37 276,8.27,8.0
277,8.68,8.93 278,8.18,8.45 279,8.25,8.82 280,8.99,8.17 281,8.36,8.17 282,8.64,8.38
283,8.94,8.77 284,8.33,8.71 285,8.23,8.81 286,8.56,8.79 287,8.71,8.89 288,8.09,8.27
289,8.93,8.0 290,8.66,8.23 291,8.35,8.1 292,8.15,8.54 293,8.72,8.03 294,8.64,8.76
295,8.94,8.28 296,8.39,8.87 297,8.01,8.4 298,8.07,8.28 299,8.12,8.65 300,8.65,8.16
数据样本的数据格式为:标号,特征值1,特征值2(没有具体含义,自动生成的数据只为能够简单的说明异常检测是怎么一回事,以及机器学习到底是如何应用在实际生产环境中)
可视化展示:
我们将数据样本投射到可视化环境中的可以看到数据呈现以下图形:
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*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" alt="" />
数据被分为3簇,在我们训练模型是K值为3簇。由于数据非常集中,数据量也非常少,同时特征向量为二维特征向量,故投影成平面图形我们一眼可以看出数据分为几簇,当样本数据的特征值很多时,就得靠计算得出K值(这里先不提)
应用代码实践:
//获取样本数据
val rawData = sc.textFile("D:/logdata/kmeans.txt")
//将样本数据转化为模型可操作的向量集
val labelAndData = rawData.map { line =>
val buffer = line.split(',').toBuffer
val label = buffer.remove(0)
val vector = Vectors.dense(buffer.map(_.toDouble).toArray)
(label, vector)
}
//将样本数据向量集缓存
val data = labelAndData.values.cache()
//建立Kmeans学习模型
val kmeans = new KMeans()
kmeans.setK(3)
//训练数据
val model = kmeans.run(data)
//打印簇心点
model.clusterCenters.foreach(println) //欧氏距离的计算函数
def distance(a: Vector, b: Vector): Double = {
math.sqrt(a.toArray.zip(b.toArray).map(p => p._1 - p._2).map(d => d * d).sum)
}
//计算向量到模型簇心点的距离
def distToCentroid(datum: Vector, model: KMeansModel) = {
val cluster = model.predict(datum)
val centroid = model.clusterCenters(cluster)
distance(centroid, datum)
}
//计算所有点到簇心点的距离集合
val distances = data.map(datum =>
distToCentroid(datum, model)
)
//获取最大的第五个值为阈值
val threshold = distances.top(5).last //测试数据获取
val testRawData = sc.textFile("D:/logdata/kmeans")
val testLabelAndData = testRawData.map { line =>
val buffer = line.split(',').toBuffer
val label = buffer.remove(0)
val vector = Vectors.dense(buffer.map(_.toDouble).toArray)
(label, vector)
}
//将测试数据集缓存
val testData = testLabelAndData.values.cache() //异常数据集过滤并打印结果
val anomalies=testData.filter { x =>
distToCentroid(x, model) > threshold
}.collect().foreach(println)
计算结果:
[5.525200003000001,5.494100009000001]
[2.522222221212122,2.512020205050505]
[8.483267326732673,8.49178217821782]
异常值:
[6.73,6.58]
[6.62,6.04]
[6.99,6.66]
[6.59,6.38]
[6.42,6.74]
[6.37,6.59]
[6.84,6.03]
[6.84,6.03]
[6.9700003,6.5299997]
[6.03,6.31]
[6.18,6.27]
[6.84,6.81]
[6.3,6.93]
[6.49,6.23]
[6.16,6.67]
[6.56,6.77]
[6.57,6.32]
[6.37,6.55]
[6.68,6.07]
[6.8,6.4]
[6.91,6.44]