Signup Login
Home Jobs Blog

Clustering Jobs in Lagos, Nigeria

View jobs that require Clustering skill on TechTalentZone
  • Ericsson logo

    Solution Architect

    EricssonLagos, Abuja, Nigeria15 May

    Ericsson (Telefonaktiebolaget L. M. Ericsson) is a Swedish multinational provider of communications technology and services. The offering comprises services, ...

  • MTN Nigeria logo

    Manager - Emerging Technologies

    MTN NigeriaLagos, Nigeria08 May

    MTN Nigeria is part of the MTN Group, Africa\'s leading cellular telecommunications company. On May 16, 2001, MTN became the first GSM network to make a ...

  • Vennote Technologies Limited logo

    Data Science & Analytics Trainer

    Vennote Technologies ..Lagos, Nigeria06 May

    Vennote Technologies Limited is a well-established ICT company with experience spanning over two decades in enterprise solutions using best of breed products ...

  • Cavista logo

    Senior Database Engineer

    CavistaLagos, Nigeria26 April

    At Cavista, our mission is to empower organizations with the world’s best technology solutions. We ensure the highest level of client satisfaction ...

  • Clickatell logo

    Infrastructure Engineer: Cloud

    ClickatellLagos, Nigeria06 April

    Clickatell is a global leader in mobile messaging and transaction services, which enable its customers to connect, interact and transact with their business ...

  • MainOne logo

    Cloud Infrastructure & Data Protection Engineer

    MainOneLagos, Nigeria21 March

    MainOne is a leading provider of innovative telecom services and network solutions for businesses in West Africa. Our world-class infrastructure enhances the ...

  • Society for Family Health (SFH) logo

    Data Scientist

    Society for Family He..Lagos, Nigeria20 March

    Society for Family Health is one of Nigeria’s largest non-governmental organisations. Founded in 1985 by three eminent Nigerians: Professor Olikoye ...

  • Geoaudits logo

    Data Analyst

    GeoauditsLagos, Nigeria20 March

    Geoaudits is a professional business process outsourcing firm in Nigeria. Geoaudits is a trusted logistics solutions partner to organizations in the private, ...

  • Engie Africa logo

    Software Engineer

    Engie AfricaLagos, Nigeria12 March

    ENGIE is a leading global energy company that builds its businesses around a model based on responsible growth to take on energy transition challenges. We ...

  • Workforce Group logo

    Head, Data Analyst

    Workforce GroupLagos, Nigeria20 February

    Workforce Management Centre Limited is a Management Consulting and Outsourcing Professional Services Firm. Following its inception in July 2004, Workforce ...

  • Advantage Health Africa logo

    Social Media / Marketing Assistant

    Advantage Health Afri..Lagos, Nigeria16 February

    Advantage Health Africa is the umbrella for these various initiatives and venture, established in January, 2017 and began full operations in July of the same ...

  • Advantage Health Africa logo

    Full Stack Developer

    Advantage Health Afri..Lagos, Nigeria10 February

    Advantage Health Africa is the umbrella for these various initiatives and venture, established in January, 2017 and began full operations in July of the same ...

  • 9Mobile logo

    Manager, Wintel

    9MobileLagos, Nigeria07 February

    9Mobile is a Nigerian telecommunications services provider, currently operating in Nigeria. 9Mobile is formerly known as Etisalat Nigeria.Job ID: IRC5335 Job ...

  • Cavista logo

    Senior Database Engineer

    CavistaLagos, Nigeria05 February

    At Cavista, our mission is to empower organizations with the world’s best technology solutions. We ensure the highest level of client satisfaction ...

  • Moniepoint Inc. (Formerly TeamApt Inc.) logo

    Data Engineer

    Moniepoint Inc. (Form..Lagos, Nigeria19 December, 2023

    Moniepoint Inc. is a leading financial technology company that provides a seamless platform for businesses to accept digital payments, access credit and access ...

  • Fidelity Bank logo

    Team Lead, Database Administration

    Fidelity BankLagos, Nigeria08 December, 2023

    Fidelity Bank is today ranked amongst the top 10 in the Nigerian banking industry, with presence in the major cities and commercial centres of Nigeria. Over ...


What is Clustering? 

Clustering is a data science technique in machine learning that groups similar rows in a data set. After running a clustering technique, a new column appears in the data set to indicate the group each row of data fits into best. Since rows of data, or data points, often represent people, financial transactions, documents or other important entities, these groups tend to form clusters of similar entities that have several kinds of real-world applications.

Applications of Clustering

  1. Data visualization: Data often contains natural groups or segments, and clustering should be able to find them. Visualizing clusters can be a highly informative data analysis approach.
  2. Prototypes: Prototypes are data points that represent many other points and help explain data and models. If a cluster represents a large market segment, then the data point at the cluster center -- or cluster centroid -- is the prototypical member of that market segment.
  3. Sampling: Since clustering can define groups in the data, clusters can be used to create different types of data samples. Drawing an equal number of data points from each cluster in a data set, for example, can create a balanced sample of the population represented by that data set.
  4. Segments for models: Sometimes the predictive performance of supervised models -- regression, decision tree and neural networks, for example -- can be improved by using the information learned from unsupervised approaches such as clusters. Data scientists might include clusters as inputs to other models or build separate models for each cluster.


Types of Clustering 

Hierarchical Clustering

Hierarchical clustering, also known as connectivity-based clustering, is based on the principle that every object is connected to its neighbors depending on their proximity distance (degree of relationship). The clusters are represented in extensive hierarchical structures separated by a maximum distance required to connect the cluster parts. The clusters are represented as Dendrograms, where X-axis represents the objects that do not merge while Y-axis is the distance at which clusters merge. The similar data objects have minimal distance falling in the same cluster, and the dissimilar data objects are placed farther in the hierarchy. Mapped data objects correspond to a Cluster amid discrete qualities concerning the multidimensional scaling, quantitative relationships among data variables, or cross-tabulation in some aspects.

Centroid-based or Partition Clustering

Centroid-based clustering is the easiest of all the clustering types in data mining. It works on the closeness of the data points to the chosen central value. The datasets are divided into a given number of clusters, and a vector of values references every cluster. The input data variable is compared to the vector value and enters the cluster with minimal difference. Pre-defining the number of clusters at the initial stage is the most crucial yet most complicated stage for the clustering approach. Despite the drawback, it is a vastly used clustering approach for surfacing and optimizing large datasets. The K-Means algorithm lies in this category. These groups of clustering methods iteratively measure the distance between the clusters and the characteristic centroids using various distance metrics. These are either Euclidian distance, Manhattan Distance or Minkowski Distance.

Density-based Clustering (Model-based Methods)

Density-based clustering method considers density ahead of distance. Data is clustered by regions of high concentrations of data objects bounded by areas of low concentrations of data objects. The clusters formed are grouped as a maximal set of connected data points. The clusters formed vary in arbitrary shapes and sizes and contain a maximum degree of homogeneity due to similar density. This clustering approach includes the noise and outliers in the datasets effectively.

Distribution Based Clustering

Distribution-based clustering creates and groups data points based on their likely hood of belonging to the same probability distribution (Gaussian, Binomial, etc.) in the data. It is a probability-based distribution that uses statistical distributions to cluster the data objects. The cluster includes data objects that have a higher probability to be in it. Each cluster has a central point, the higher the distance of the data point from the central point, the lesser will be its probability to get included in the cluster. Distribution-based clustering has a vivid advantage over the proximity and centroid-based clustering methods in terms of flexibility, correctness, and shape of the clusters formed. The major problem however is that these clustering methods work well only with synthetic or simulated data or with data where most of the data points most certainly belong to a predefined distribution, if not, the results will overfit.