clustering   9566

« earlier    

Crossplane - Open Source Multicloud Control Plane
Open source multicloud control plane. It introduces workload and resource abstractions on-top of existing managed services that enables a high degree of workload portability across cloud providers. A single crossplane enables the provisioning and full-lifecycle management of services and infrastructure across a wide range of providers, offerings, vendors, regions, and clusters. Crossplane offers a universal API for cloud computing, a workload scheduler, and a set of smart controllers that can automate work across clouds.
Kubernetes  golang  opensource  automation  clustering  API  deployment 
5 days ago by liqweed
The Most Important Data Science Tool for Market and Customer Segmentation
K-Mean implementation. GitHub Gist: instantly share code, notes, and snippets.
business  clustering  data-science  python  visualization 
8 days ago by id1
Swisscows
- the data secure search engine from Switzerland. No monitoring. No data storage.
- name was originally Hulbee
internet  search  engine  privacy  clustering  semantic 
11 days ago by jyllsy
Minnesota police officers convicted of serious crimes still on the job - StarTribune.com
behind the scenes from Nick Diachopoulos in CJR. // Unsupervised approaches to grouping or clustering can sometimes be made more efficient by providing targeted feedback to the machine-learning system. For instance, Dedupe, a tool for grouping and linking noisy records, has been used by investigative journalists at the Minneapolis StarTribune for its “Shielded by the Badge” series. Dedupe uses an approach called active learning. As the system tries to cluster items together, it asks for feedback from a human trainer on the items it’s least confident about. This maximizes the value of human feedback for improving the results over time.
unsupervisedmachinelearning  unsupervised  journalism  police  activelearning  clustering  feedback  startribune  dedupe  machinelearning  artificialIntelligence  maryjowebster 
19 days ago by fcoel
Onion Curve: A Space Filling Curve with Near-Optimal Clustering
"Space filling curves (SFCs) are widely used in the design of indexes for spatial and temporal data. Clustering is a key metric for an SFC, that measures how well the curve preserves locality in moving from higher dimensions to a single dimension. We present the onion curve, an SFC whose clustering performance is provably close to optimal for the cube and near-cube shaped query sets, irrespective of the side length of the query. We show that in contrast, the clustering performance of the widely used Hilbert curve can be far from optimal, even for cube-shaped queries. Since the clustering performance of an SFC is critical to the efficiency of multi-dimensional indexes based on the SFC, the onion curve can deliver improved performance for data structures involving multi-dimensional data."
spatial  clustering 
20 days ago by aapl
Weave Scope - Automatically Detect and Process Containers And Hosts
Zero configuration or integration required — just launch and go.
Weave Scope automatically detects processes, containers, hosts. No kernel modules, no agents, no special libraries, no coding. Seamless integration with Docker, Kubernetes, DCOS and AWS ECS.
Kubernetes  visualization  clustering  microservices  opensource  monitoring  tools 
4 weeks ago by liqweed
KubeView - Kubernetes cluster visualiser and graphical explorer
Kubernetes cluster visualiser and visual explorer. KubeView displays what is happening inside a Kubernetes cluster, it maps out the API objects and how they are interconnected. Data is fetched real-time from the Kubernetes API. The status of some objects (Pods, ReplicaSets, Deployments) is colour coded red/green to represent their status and health.

The app auto refreshes and dynamically updates the view as new data comes in or changes.
Kubernetes  visualization  clustering  opensource  microservices  monitoring 
4 weeks ago by liqweed
Model based clustering and classification
"Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics."
to:NB  books:noted  classifiers  clustering  statistics  raftery.adrian 
4 weeks ago by cshalizi
KubeOne - Lifecycle management tool for Highly-Available Kubernetes clusters
A CLI tool and a Go library for installing, managing, and upgrading Kubernetes High-Available (HA) clusters. It can be used on any cloud provider, on-prem or bare-metal cluster.
Kubernetes  deployment  automation  opensource  clustering  tools  distributed 
4 weeks ago by liqweed
KEDA - Kubernetes-based Event Driven Autoscaling
Provides event driven scale for any container running in Kubernetes.

KEDA allows for fine grained autoscaling (including to/from zero) for event driven Kubernetes workloads. KEDA serves as a Kubernetes Metrics Server and allows users to define autoscaling rules using a dedicated Kubernetes custom resource definition.

KEDA can run on both the cloud and the edge, integrates natively with Kubernetes components such as the Horizontal Pod Autoscaler, and has no external dependencies.
Kubernetes  performance  automation  opensource  clustering 
5 weeks ago by liqweed
How to Use t-SNE Effectively
by a Google team, on Distill.pub. This is a pretty slick article / web application that explains some common pitfalls in the application of t-SNE through a series of simple, interactive examples. I liked this a lot, and learned that the perplexity parameter, in particular, can really affect whether or not clusters even appear in a t-SNE plot.
machine-learning  clustering 
6 weeks ago by DGrady

« earlier    

related tags

2019  activelearning  administration  adversarial-tricks  ai  algorithm  algorithms  alpha  alshaw  analysis  anomaly  aos8  api  apriori  arbitrage  architecture  artificialintelligence  aruba  authentication  automation  autoscaling  azure  bioinformatics  blog  books:noted  breakingnews  business  cache  canvas  categorical  class  classification  classifiers  cloud  cluster-analysis  cluster  code  combinatorics  consider:genetic-programming  consider:nonbiological-genomes  d3  dask  data-analysis  data-pageant  data-science  data-structures  data-visualization  data  database  datamining  datascience  dataviz  dedupe  deduplication  deep-learning  dendrograms  deployment  dev  devops  diagrams  digits  distributed-systems  distributed  distributedsystems  docker  eigenvalue  elasticsearch  elk  encryption  engine  etcd  exploration  failover  feature-construction  feature-intuiting  feature-selection  feedback  filebeat  finance  frequent  games  gas  ggplot2  glusterfs  golang  graph-theory  graph  hadr  high-availability  highavailability  howto  hypergraphs  image-processing  indexing  informationretrieval  infovis  innodbcluster  internet  intro  itemsets  izito  javascript  journalism  k-means  k8s  kith_and_kin  kmeans  knn  kubernetes  latent  lca  lda  learning  linguistics  logging  logs  logstash  machine-learning  machine  machine_learning  machinelearning  machinelerning  maryjowebster  mesos  meta  metasearch  microservices  microsoft  ml  mnist  monitoring  mqtt  music  mysql  mysqlshell  network  networking  networks  neural-networks  nlp  oidc  on-prem  openid  opensource  osdev  pca  performance-measure  performance  plan9  police  postgresql  prediction  privacy  programming  propublica  proxy  python  pytorch  r-project  r  raftery.adrian  raspberry-pi  raspberrypi  rather-interesting  rdbms  redis  reference  representation  reuters  review  rinaldo.alessandro  rnaking  rock  rstats  satelliteimagery  scale  search  searchengine  security  semantic  serf  server  similarity  social-dynamics  social-networks  socialmedia  spatial  spectral_clustering  sql-server  startribune  statistics  statsproj  storage  svg  swim  sysadmin  text  textanalysis  textbook  theory  time-series  timeseries  to-write-about  to:nb  tonydarnell  tool  tools  toread  tracer  tsne  tutorial  twitter  unix  unsupervised-learning  unsupervised  unsupervisedmachinelearning  vagrant  vectors  vernemq  virtualbox  virtualization  visualization  wasserman.larry  windows-server  windows  windowsserver  witness  word2vec  workflow  wsfc  xai  youtube 

Copy this bookmark:



description:


tags: