歡迎來到全傑科技

今日訪客: 0
線上訪客:

本軟體最新開課活動

目前沒有相關活動!!!
本軟體之前的活動!!
本軟體有產品說明會, 請洽本公司!!

下載專區 Download

活動資訊

  • 目前尚無任何訓練課程!!

聯絡我們

姓名:
Email:
聯絡電話:
單位:
部門:
附件:
您的留言:

提供專業軟體代購服務
如有未列於網站之產品需求
歡迎來電洽詢,感謝您!
電話:(02)2507-8298

Cubist 2.08
數值預測資料採礦軟體
Rule-based models for numerical prediction
軟體代號:7647
瀏覽次數:9
LinuxWindowsXPWindowsVISTAWindows7
教育版
商業版
試用版
遠端展示
產品說明會
教育訓練
教學範例檔
教育訓練光碟
一年授權
永久授權
中文型錄
英文型錄
網路啟動
合法保證
電子英文手冊
產品介紹!

Cubist是一個資料採礦軟體,專門用於數值預測,可以處理大量的資料,可高達數十萬筆資料,而且非常容易使用,您不須有特別的統計知識就可使用,本公司也會有提供教育訓練課程協助您,Cubist很類似統計上的多變量線性迴歸,也比類神經預測更容易理解,為加速計算,本軟體也支援多CPU和多核心的電腦,並可用於WinXP、VISTA、7和Linux。Cubist也提供批次執行的功能,讓您一次處理數十項工作,資料是以.CSV檔的ASCII格式呈現,您可以使用EXCEL來處理您的資料。

Cubist 功能特色:

Cubist has been designed to analyze substantial databases containing hundreds of thousands of records and tens to thousands of numeric or nominal fields. If you have used neural networks or similar modeling tools, you'll be surprised by Cubist's speed!  

To maximize interpretability, Cubist models are expressed as collections of rules, where each rule has an associated multivariate linear model. Whenever a situation matches a rule's conditions, the associated model is used to calculate the predicted value.

Cubist is available for Win2000/Xp/Vista/7 and Linux 

Cubist is easy to use and does not presume advanced knowledge of Statistics or Machine Learning. 

‧RuleQuest provides C source code so that models constructed by Cubist can be embedded in your organization's own systems.

Cubist 應用範例:

Housing Prices in Boston、The Fat Content of Meat、Concrete Compressive Strength、The Age of Abalone、A Simple Time Series Example: Fraser River、A Larger Example : El Niño


Sample:

Statlib is a central repository used by statisticians. One of the datasets obtainable from this interesting site concerns estimating the fat content of meat samples using absorbency in the near infrared spectrum. This data comes from the Tecator Infratec Food and Feed Analyzer using 100 channels. Each attribute consists of the value of the instrument reading in one channel, so this is a high-dimensional prediction task.

Cubist derives a model with four rules from 240 training examples (again in less than 0.1 seconds):

Rule 1: [13 cases, mean 7.577, range 1.7 to 15.9, est err 0.933]
    if
        A05 > 3.17348
        A89 <= 3.82829
    then
        Fat = -7.32 - 3037 A38 + 2932.7 A37 - 3411.5 A13 + 2798.8 A53
              + 2372.8 A39 + 2885.3 A12 - 2183.8 A54 - 1856.9 A34 - 1496.3 A98
              - 1453.4 A40 - 1859.6 A05 + 1229.8 A99 + 1146.5 A57 + 980.3 A60
              - 996.9 A52 - 949.7 A58 + 1090.4 A09 + 930.8 A30 + 801.6 A44
              - 857.7 A28 - 723.3 A61 - 623.2 A95 + 477.5 A97 + 495.5 A25
              + 560 A07 - 445.4 A48 + 547.1 A00 + 416.4 A36 - 330.8 A45
              + 326.5 A49 + 303.4 A93 + 256.8 A90 - 176.6 A50 - 112.5 A89
              - 32.8 A81

Rule 2: [129 cases, mean 11.667, range 0.9 to 36.2, est err 0.833]
    if
        A40 <= 3.08971
    then
        Fat = 7.263 + 6060 A38 - 5593.9 A37 + 5348.6 A36 + 4581.8 A53 + 5121 A12
              - 3990.6 A40 - 3798.5 A34 - 4133.9 A05 - 2824.2 A95 - 2866.6 A52
              - 3267.1 A17 + 2744.8 A60 + 2497.6 A97 - 2793.3 A13 - 2189.4 A58
              - 2056.2 A54 - 2003.8 A70 + 2424 A07 - 1877 A30 + 1722.9 A39
              + 1705 A76 + 1582.1 A28 + 1595.4 A25 - 1210.7 A98 + 1079.5 A57
              + 942.4 A99 + 1026.7 A09 - 687.9 A61 + 635.7 A44 + 515.1 A00
              - 350.3 A45 + 241.7 A90 - 179.7 A49

Rule 3: [35 cases, mean 26.331, range 10 to 56.6, est err 1.601]
    if
        A05 > 3.17348
        A89 > 3.82829
    then
        Fat = 14.568 + 6740.3 A39 + 6256.3 A49 - 5734 A48 - 5374.5 A38
              - 5371.3 A34 - 4599.1 A40 + 4257.9 A36 - 3282.7 A95 + 3508 A30
              + 3102.5 A93 - 2987 A98 + 2990.2 A99 - 2766 A28 - 2033.9 A50
              + 1903.8 A37 + 1816.9 A53 + 1731.1 A44 - 1925.9 A13 + 1873 A12
              - 1417.7 A54 - 1419.4 A05 + 928 A25 + 744.3 A57 + 636.4 A60
              - 647.2 A52 - 616.5 A58 + 707.9 A09 + 519.8 A45 - 400.1 A61
              - 335.7 A81 + 310 A97 + 363.5 A07 + 355.2 A00 + 166.7 A90

Rule 4: [63 cases, mean 30.403, range 2.9 to 58.5, est err 1.727]
   if
        A05 <= 3.17348
        A40 > 3.08971
    then
        Fat = 10.747 - 12872.2 A38 + 11360 A37 + 10884.5 A39 + 10841.4 A53
              - 11492 A13 + 11176.6 A12 - 8549.5 A34 - 8459.4 A54 - 7322.5 A40
              - 8778.5 A05 - 6452.5 A98 + 5477.3 A99 + 4441.1 A57 + 4477.9 A30
              + 4813.9 A09 + 3797.3 A60 - 3861.7 A52 - 3727.5 A95 - 4041.9 A28
              - 3678.7 A58 + 3499.4 A44 + 3038.2 A36 - 2779.3 A61 - 2742.6 A48
              + 2451.8 A49 + 2121.4 A93 + 2116.7 A25 + 1849.6 A97 + 2169.1 A07
              + 2119.4 A00 - 1244.4 A45 - 1101.8 A50 + 994.6 A90 - 294.8 A16
              - 229.5 A81