溫馨提示:需求數量不同,價格不同。請聯系我們,確認當前新的報價!
VisuMap是針對探索性分析問題的面向可視化的解決方案。VisuMap體現了多項數學和編程技術,提供:
RPM(Relational Perspective Map)、PCA(Principal Component Analysis)、MDS(Multidimensional Scaling)等映射和降維算法的集合;
用于高維數據的高等聚類算法,如自組織映射、親和傳播、 k-均值聚類;
動態鏈接的數據視圖;和
用于高等應用程序的腳本和庫接口。
VisuMap幫助人們理解高維、復雜、大型或其他“困難”的數據集。它使研究人員、分析師和其他人員能夠通過與數據的2D和3D地圖進行交互來掌握趨勢和模式。
通過集成新的數學技術來呈現相互關聯的多維數據,VisuMap使用戶能夠運用他們視覺感知和直覺的力量。
這種對人類感知能力的利用,加上易用性,意味著VisuMap在傳統數據挖掘工具失敗的地方取得了成功。同時,開發人員和系統管理員會發現廣泛的腳本和插件界面,鼓勵他們自定義、集成和擴展VisuMap以滿足用戶的需求。
多維縮放(數據映射):
主成分分析(PCA)
多維尺度(Sammon 地圖)
關系透視圖(RPM)
曲線成分分析(CCA)
通過SMACOF算法的MDS
對應分析
隨機領域嵌入(t-SNE)
線性判別分析(LDA)
親和嵌入
數據庫+
數據聚類:
自組織映射(Kohonen網)
K-均值聚類
公制抽樣
凝聚聚類
親和傳播
自組織圖
譜聚類
均值漂移聚類
集成聚類
k-Nearst Neighbor(k-NN) 聚類
數據可視化與探索:
13個內置指標:euclidean, mahalanobis, pearson correlation, speaman ranking, wedge- hedge,等
動態鏈接的數據視圖:條形圖/曲線圖、頻譜圖、表格、樹、Shepard圖、地圖集、儀表板、熱圖等
交互式參數探測
屬性圖分析
硬件加速3D動畫
編程接口:
用于自動化的腳本接口
上下文相關的腳本編輯器
用于擴展的DotNet插件接口
導入/導出格式:ASCII、SQL-DB、JPEG、GIF、SVG等(10種圖像格式)
數據建模/機器學習:
深度分類
深度剖析
應用
藥劑學
生物信息學
財務分析
市場分析
電訊業
系統要求
Windows 7及更高版本
Microsoft .NET 4.8運行時
DirectX 12兼容顯卡
【英文介紹】
VisuMap helps people understand high-dimensional, complex, large or otherwise 'difficult' datasets. It enables researchers, analysts and other professionals to grasp trends and patterns by interacting with 2D and 3D maps of their data.
By integrating the latest mathematical techniques for presenting multiple dimensions of data in relation to eath other, VisuMap enables users to apply the power of their visual perception and intuition.
This harnessing of human perceptual power, together with unprecedented ease of use, means that VisuMap has succeeded where conventional data mining tools have failed. At the same time, developers and system administrators will find extensive scripting and plug-in interfaces that encourage them to customize, integrate and extend VisuMap to suit their end-users' needs.
Multidimesnional Scaling (data mapping):
Principal Component Analysis (PCA)
Multidimensional Scaling (Sammon Map)
Relational Perspective Map (RPM)
Curvilinear Component Analysis (CCA)
MDS by SMACOF Algorithm
Correspondence Analysis
Stochastic Neighbor Embedding (t-SNE)
Linear Discreminate Analysis (LDA)
Affinity Embedding
DBSCAN+
Data Clustering:
Self-Organizing Map (Kohonen Net)
K-Mean Clustering
Metric Sampling
Agglomerative Clustering
Affinity Propagation
Self-Organzing Graph
Spectral Clustering
Mean-Shift Clustering
Ensemble Clustering
k-Nearst Neighbor (k-NN) Clustering
Data Visualization & Exploration:
13 built-in metrics: euclidean, mahalanobis, pearson correlation, speaman ranking, wedge- hedge, etc.
Dynamically linked data views: bar/curve charts, spectrum, table, tree, Shepard diagram, atlas, dashboard, heatmap, etc..
Interactive parameter probing
Attribute map analysis
Hardware accelerated 3D animation
Programming Interfaces:
Scripting interface for automation
Context sensitve scriptor editor
DotNet plug-in interface for extension
Import/Export formats: ASCII, SQL-DB,
JPEG, GIF, SVG etc.(10 image formats).
Data Modeling/Machine Learning:
Deep Classification
Deep Profiling
Applications
Pharmaceutics
Bioinformatics
Financial analysis
Market analysis
Telecommunication industry