And now, let's practice tricky clusters in a row.

现在，让我们连续练习一些复杂的聚。

For example, this clustering phenomenon led geneticists to a gene associated with LDL cholesterol (the bad kind) and another related to inflammation.

，聚现象导致基因学家发现了一与低密度脂蛋白胆固醇(一有害的胆固醇)有关的基因，另一与炎症有关。

It's about us learning to cluster. What does that mean?

是关于我们学习聚。 那是什么意思？

It is able to cluster cells according to their positions in such a multi-dimensional space.

它能够根据细胞在多维空间中的位置对它们进行聚。

Okay, so it's clustering proteins that seem to act in concert.

好的，所以是将那些乎协同作用的蛋白质进行聚。

OK, so this is when we use machine learning algorithms to analyze and cluster unlabeled data sets, and this method helps us discover hidden patterns or groupings without the need for human intervention, right?

好的， 就是我们使用机器学习算法来分析和聚无标签数据集的情况，方法有助于我们发现隐藏的模式或分组， 而无需人工干预，对吧？

Brilliant's new course on data clustering, for example, equips you with the same tools security researchers like Karsten use to spot trends among the billions of SS7 messages.

， Brilliant 新推出的数据聚课程，为你提供了与安全研究员 Karsten 所使用的相同工具， 用于从数十亿的 SS7 消息中识别趋势。

But of course, if we go back to the clustering method that seems very crude and primitive, well, you could just download all those centroids, right?

当然， 果我们回到那非常粗糙和原始的聚方法，那么你完全可以直接下载所有的质心，对吧？

These clusters are then connected to each other by an algorithm, and 113 million line segments , which reveal new conceptual relationships.

些聚随后通过算法相互连接，形成了1.13亿条线段， 揭示了新的概念关系。

Research on Lightweight Citrus Flowering Rate Statistical Model Combined with Anchor Frame Clustering Optimization[J].

轻量级柑橘开花率统计模型结合锚框聚优化的研究[J]。

So the cat and dog or two crops of this image, they belong to the same cluster.

因此，猫和狗或幅图像的两个裁剪部分，它们属于同一个聚。

If we look closely at this clump, we can see that it's sorted along two axes.

果我们仔细观察个聚，我们可以到它是沿着两个轴排序的。

They used an algorithm called DBSCAN, Density Based Spatial Clustering.

他们使用了一名为DBSCAN（基于密度的聚算法）的算法。

It is able to cluster cells according to their positions in such a multidimensional space, and from the way they cluster, it can sort them into baskets corresponding to cell types.

它能够根据细胞在样的多维空间中的位置进行聚，并根据它们的聚方式，将它们分到与细胞型相对应的篮子中。

So if you do the exercise this way, the false alarm probability of the clustering on which the Planet 9 hypothesis is built is about 0.4%.

所以果你用方式进行分析，构成第九行星假设的聚的误报概率约为0.4%。

Type 3 neighborhood clustering involves looser packing.

第三邻域聚表现为较为松散的排列方式。

So basically, you can be 0.2 to cluster number one and 0.8 to cluster number two, so it's not really hard.

所以基本上，你可以有0.2的概率属于第一个聚，有0.8的概率属于第二个聚，所以它并不是真正的硬聚。

So a non contrastive method, for example, like clustering is one non contrastive method.

因此，非对比方法之一，聚，便是一非对比方法。

So as you're going through the data, you're actually computing these clusters online.

因此，当你遍历数据时，实际上是在线计算些聚。

What that means is, if you have a data set of N examples, you would run over all of these N examples, get features for them, perform clustering, so basically get some clusters, and then repeat the process again.

意味着，果你有一个包含N个样本的数据集，你需要遍历所有些N个样本，提取它们的特征， 进行聚操作，从而得到一些聚结果， 然后再次重复一过程。