Tag: 2020 (Page 2 of 2)

k-Means: One of the simplest Clustering Algorithms

One of the most popular unsupervised clustering methods is the k-means algorithm. It is considered one of the easiest and most cost-effective clustering algorithms to create. It is therefore well suited to identify an overview of possible patterns in data.

What is the principle behind the k-means algorithm?
in this article we will explain what is behind this algorithm and how it really works, because, the better you know your data science tools, the better you will be able to analyze your data.

What is k-Means?

The k-means algorithm described by MacQueen, 1967 goes back to the methods described by.
Lloyd, 1957 and Forgy, 1965 described methods. The unsupervised machine learning algorithm is used for vector quantification or cluster analysis. If you don’t know what the differences are between supervised, unsupervised and reinforcement methods, read this article on the main machine learning categories.

The following figure shows the basic principle of the k-Means clustering algorithm.

The figure shows the basic principle of the k-Means clustering algorithm.
Basic principle of the k-Means clustering

The main goal of unsupervised clustering is to create collections of data elements that are similar to each other, but dissimilar to elements in other clusters.

What is the principle behind the k-means algorithm?

Here, a data set is partitioned into k groups with equal variance. The number of clusters searched for must be specified in advance. Each disjoint cluster is described by the average of all contained samples.
The so-called cluster centroid.
The following figure shows the cluster center of gravity principle.

The figure shows the k-Means cluster center of gravity principle.
cluster center of gravity principle

Each centroid is updated to represent the average of its constituent instances. This is done until the assignment of instances to clusters does not change.

Applied algorithm process

But how exactly does the algorithm work?
First, initial centroids are set. The distances between data instances and centroids are measured and data instances are added as members of the nearest centroid. The centroids are recalculated. If necessary, final centroids are re-measured, re-clustered or re-calculated

The figure shows the process of k-Means Centroid creation
Centroid creation

That is why Liquid State Machines (LSM) are great

– Recently developed computational model

– does not require information to be stored in some stable state of the system

→ the inherent dynamics of the system are used by a memory less readout function to compute the output

→ can be used for complex Tasks (pattern classification, function approximation, object tracking, …)

LSMs take the temporal aspect of the input into account


The figure shows a typical structure of a liquid State Machine.
Liquid State Machine

Reservoir/ Liquid

– large accumulation of recurrent interacting nodes
→ is stimulated by the input layer
– Liquid itself is not trained, but randomly constructed with the help of heuristics
– Loops cause a short-term memory effect
– preferably a Spiking Neural Network (SNNs)
→ are closer to biological neural networks than the multilayer Perceptron
→ can be any type of network that has sufficient internal dynamics

Running State

→ will be extracted by the readout function

– depend on the input streams they’ve been presented

Readout Function

– converts the high-dimensional state into the output

– since the readout function is separated from the liquid, several readout functions can be used with the same liquid

→ so different tasks can be performed with the same input

lsm readout fcts
different types of readout functions

AutoEncoder – What Is It? And What Is It Used For?

AutoEncoder – In data science, we often encounter multidimensional data relationships. Understanding and representing these is often not straightforward. But how do you effectively reduce the dimension without reducing the information content?

Unsupervised dimension reduction

One possibility is offered by unsupervised machine learning algorithms, which aim to code high-dimensional data as effectively as possible in a low-dimensional way.
If you don’t know the difference between unsupervised, supervised and reinforcement learning, check out this article we wrote on the topic.

What is an AutoEncoder?

The AutoEncoder is an artificial neural network that is used to unsupervised reduce the data dimensions.
The network usually consists of three or more layers. The gradient calculation is usually done with a backpropagation algorithm. The network thus corresponds to a feedforward network that is fully interconnected layer by layer.


AutoEncoder types are many. The following table lists the most common variations.

The figure shows all common AutoEncoder types
AutoEncoder types

However, the basic structure of all variations is the same for all types.

Basic Structure

Each AutoEncoder is characterized by an encoding and a decoding side, which are connected by a bottleneck, a much smaller hidden layer.

The following figure shows the basic network structure.

The figure shows the basic AutoEncoder structure.
AutoEncoder model architecture

During encoding, the dimension of the input information is reduced. The average value of the information is passed on and the information is compressed in such a way.
In the decoding part, the compressed information is to be used to reconstruct the original data. For this purpose, the weights are then adjusted via backpropagation.
In the output layer, each neuron then has the same meaning as the corresponding neuron in the input layer.

Autoencoder vs Restricted Boltzmann Machine (RBM)

Restricted Boltzmann Machines are also based on a similar idea. These are undirected graphical models useful for dimensionality reduction, classification, regression, collaborative filtering, and feature learning. However, these take a stochastic approach. Thus, stochastic units with a particular distribution are used instead of the deterministic distribution.

RBMs are designed to find the connections between visible and hidden random variables. How does the training work?
The hidden biases generate the activations during forward traversal and the visible layer biases generate learning of the reconstruction during backward traversal.


Since the random initialization of weights in neural networks at the beginning of training is not always optimal, it makes sense to pre-train. The task of training is to minimize an error or a reconstruction in order to find the most efficient compact representation for input data.

The figure shows the pretraining procedure of an autoencoder according to Hinton.
Training Stacked Autoencoder

The method was developed by Geoffrey Hinton and is primarily for training complex autoencoders. Here, the neighboring layers are treated as a Restricted Boltzmann Machine. Thus, a good approximation is achieved and fine-tuning is done with a backpropagation.

SciPy turns Python into an ingenious free MATLAB alternative

Python vs MATLAB

== Open source Python library
– a collection of mathematical algorithms and convenience functions

– is mainly used by scientists, analysts and engineers for scientific computing, visualization and related activities

– Initial Realease: 2006; Stable Release: 2020
– depends on the NumPy module
→ basic data structure used by SciPy is a N-dimensional array provided by NumPy


scipy benefits


– SciPy library provides many user-friendly and efficient numerical routines:

scipy subpackages

Available sub-packages

SciPy ecosystem

– scienitific computing in Python builds upon a small core of open-source software for mathematics, science and engineering

scipy ecosystem
SciPy Core Software

More relevant Packages

– the SciPy ecosystem includes, based on the core properties, other specialized tools

scipy eco sidepackages

The product and further information can be found here:

Apache Mahout – A Powerful Open Source Machine Learning Project

Apache Mahout is a powerful machine learning tool that comes with a seamless compatibility to the strong big data management frameworks from the Apache universe. In this article, we will explain the functionalities and show you the possibilities that the Apache environment offers.

What is Machine Learning?

Machine learning algorithms provide lots of tools for analyzing large unknown data sets.
The art of data science is to extract the maximum amount of information depending on the data set by using the right method. Are there patterns in the high-dimensional data relationships, and how can they be represented in a low-dimensional way without much loss of information?

scikitLearn ml
Fields of machine learning

There is often a similar amount of information in the failure as when an algorithm was able to successfully create groupings.
It is important to understand the mathematical approaches behind the tools in order to draw conclusions about why an algorithm did not work.
If you don’t know the basic machine learning categories, it’s best to read our article on the subject first.

Machine Learning and Linear Algebra

Most machine learning methods are based on linear algebra.
This mathematical subfield deals with linear transformations, vector spaces and linear mappings between them.
The knowledge of the regularities is the key to the correct understanding of machine learning algorithms.

What is Apache Mahout

Apache Mahout is an open source machine learning project that builds implementations of scalable machine learning algorithms with a focus on linear algebra. If you’re not sure what Apache is, check out this article. Here we introduce you to the project and its main projects once.

Mahout was already released in 2009 and since then it is constantly extended and kept up-to-date by a very active community.
Originally, it contained scalable algorithms closely related to Apache Hadoop and MapReduce.
However, Mahout has since evolved into a backend independent environment. That is, it operates on non-Hadoop clusters or single nodes.


The math library is based on Scala and provides an R-like Domain Specific Language (DSL). Mahout is usable for Big Data applications and statistical computing. The figure below lists all machine learning algorithms currently offered by Mahout.

The figure below lists all machine learning algorithms currently offered by Apache Mahout.
Implemented mathematical functions and algorithms

The algorithms are scalable and cover both supervised and unsupervised machine learning methods, such as clustering algorithms.

Apache Mahout covers a large part of the usual machine learning tools. This means that data can be analyzed without having to change frameworks. This is a big plus for maintaining compatibility in the application.

Apache Ecosystem

The framework integrates seamlessly into the Apache Ecosystem. This means that an application can access the entire power of the data processing platforms and build very high-performance big data pipelines. The following figure shows the Apache data management ecosystem.

Apache Mahout ecosystem
Apache Mahout ecosystem

Through connectivity to Apache Flink, stream data analysis pipelines can be built, or with Hive data from relational databases can be automatically converted into MapReduce or Tez or Spark jobs.

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