axborotizlashdaklasterlash

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prezentatsiya powerpoint muhammad al-xorazmiy nomidagi toshkent axborot texnologiyalari universiteti axborotlarni izlash vaajratib olish swd 1316 axborot izlashda klasterlash. axborot izlashda klasterlash, muammo bayonoti. ma’ruza rejasi hujjatlarning vektor fazo modeli klasterlash tushunchasi klasterlash usullari k-means klasterlash usuli k-means bo‘yicha amaliy masala hujjatlarning vektor fazo modeli tf (term frequency) – atama chastotasi idf (inverse document frequency)- hujjatning teskari chastotasi tf-idf (term frequency- inverse document frequency) – atamaning hujjatga tegishlilik ko’rsatkichi hujjatlarning vektor fazo modeli doc1= [tf1, tf2 ,…., tfn] doc2= [tf1, tf2 ,…., tfn] doc3= [tf1, tf2 ,…., tfn] doc4= [tf1, tf2 ,…., tfn] doc5= [tf1, tf2 ,…., tfn] doc6= [tf1, tf2 ,…., tfn] … doc1= [tf-idf1, tf-idf2 ,…., tf-idfn] doc2= [tf-idf1, tf-idf2 ,…., tf-idf n] doc3= [tf-idf1, tf-idf2 ,…., tf-idf n] doc4= [tf-idf1, tf-idf2 ,…., tf-idf n] doc5= [tf-idf1, tf-idf2 ,…., tf-idf n] doc6= [tf-idf1, tf-idf2 ,…., tf-idf n] … tf orqali ifodasi tf-idf orqali ifodasi hujjatlarning vektor fazo modeli doc1= …

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usllari grid-based methods - gridga asoslangan usullar: hierarchical based methods - ierarxiyaga asoslangan usullar: klasterlash usullari aglomerativ ierarxik klasterlash ierarxik klasterlash algoritmining eng keng tarqalgan turi. u ob’ektlarni bir-biriga qanchalik o‘xshashligiga qarab klasterlarda guruhlash uchun ishlatiladi. klasterlash usullari (ierarxik klasterlsh) distribution-based methods - tarqatishga asoslangan klasterlash: fuzzy clustering methods – noaniq klasterlash usullari: klasterlash usullari dbscan (density-based spatial clustering of applications with noise) shovqinli ilovalarning zichlikka asoslangan fazoviy klasterini anglatadi. bu klasterlash usullari (dbscan) gauss aralashmasi modeli (gmm) klasterlash va zichlikni baholash uchun ishlatiladigan ehtimollik modelidir. bu ma'lumotlar har biri alohida klasterni ifodalovchi bir nechta gauss taqsimotlarining aralashmasidan yaratilgan deb taxmin qiladi. klasterlash usullari (gaus aralashmasi modeli) k-means eng taniqli va mashhur klasterlash algoritmi: taxminiy dastlabki klaster markazlari tanlang iteratsiya: har bir misolni eng yaqin markazga belgilang / klasterlang klasterdagi nuqtalarning o‘rtacha qiymati sifatida markazlarni qayta hisoblang k-means: misol k-means uchun 1-centroidni tasodifiy ravishda tanlash 1 k-means uchun qolgan k …

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2,8) 10 (3,1) 4 (3,6) 7 (4,3) 3 (4,7) 7 (6,2) 0 (6,8) 6 (8,6) 6 (9,4) 5 (10,6) 8 (11,3) 6 (13,6) 11 (2,8) 1 (6,2) (4,7) (6,8) (8,6) (10,6) (9,4) (11,3) (3,1) (4,3) (2,3) (3,6) (13,6) 2 1-centroiddan eng uzoq bo‘lgan masofani tanlash 2-centroidni tanlash va o‘rnatish (2,8) 1 (6,2) (4,7) (6,8) (8,6) (10,6) (9,4) (11,3) (3,1) (4,3) (2,3) (3,6) (13,6) 2 1 yoki 2 centroidga nisbatan eng yaqinlaridan eng uzoqdagisini tanlash (2,8) 1 (6,2) (4,7) (6,8) (8,6) (10,6) (9,4) (11,3) (3,1) (4,3) (2,3) (3,6) (13,6) 2 13 10 15 4 14 5 to‘plam kordinatasi 1-centroiddan masofasi (𝑪𝒆𝒏𝟏) 2-centroiddan masofasi (𝑪𝒆𝒏𝟐) 3-centroid uchun masofasi min (μ1) (2,3) 5 14 5 (2,8) 10 13 10 (3,1) 4 15 4 (3,6) 7 10 7 (4,3) 3 12 3 (4,7) 7 10 7 (6,2) 0 11 0 (6,8) 6 9 6 (8,6) 6 5 5 (9,4) 5 6 5 (10,6) 8 …

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rtacha qiymati : (c)  1  x bu yerda: x  y  n i1 xi  yi  i n x x c c i1   𝐶𝑒𝑛𝑡𝑟𝑜𝑖𝑑 = 1 1 𝐶 ෍ 𝑥𝑖 , 𝐶 ෍ 𝑦𝑖 𝐶𝑒𝑛𝑡𝑟𝑜𝑖𝑑1 = = (4.8, 2.6) 𝐶𝑒𝑛𝑡𝑟𝑜𝑖𝑑2 = 2+3+4+6+9 , 3+1+3+2+4 5 5 8+10+11+13 , 6+6+3+6 4 4 = (10.5, 5.25) 𝐶𝑒𝑛𝑡𝑟𝑜𝑖𝑑3 = 2+3+4+6 , 8+6+7+8 4 4 = (3.75, 7.25) centroidlarni qayta o‘rnatish 1 (2,8) (13,6) (6,2) (6,8) (8,6) (10,6) (9,4) (11,3) (3,1) (4,3) (2,3) (3,6) 2 (4,7) 3 to‘plam kordinatasi 1-centroiddan masofasi (4.8, 2.6) 2-centroiddan masofasi (10.5, 5.25) 3-centroiddan masofasi (3.75, 7.25) ajratilgan klasteri (2,3) 3,2 10,75 6 1-klaster (2,8) 8,2 11,25 2,5 3-klaster (3,1) 3,4 11,75 7 1-klaster (3,6) 5,2 8,25 2 3-klaster (4,3) 1,2 8,75 4,5 1-klaster (4,7) 5,2 8,25 0,5 3-klaster (6,2) 1,8 7,75 7,5 1-klaster (6,8) 6,6 7,25 3 3-klaster (8,6) 6,6 3,25 5,5 2-klaster …

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(11,3) (3,1) (4,3) (2,3) (3,6) 2 (4,7) 3 qayta o‘rnatilgan centroidga nisbatan klasterlash klasterlash natijasi 1 (2,8) (13,6) (6,2) (6,8) (8,6) (10,6) (9,4) (11,3) (3,1) (4,3) (2,3) (3,6) 2 (4,7) 3 43 image1.png image2.png image11.png image12.png image13.png image14.png image15.png image16.png image17.png image18.png image19.png image20.png image3.png image21.png image22.png image23.png image24.png image25.png image4.png image5.png image6.png image7.png image8.png image9.png image10.png image26.png image27.jpg image28.png image29.png image30.png image31.png image32.png image33.png image34.png image35.png image36.png image37.jpg image38.png image39.png image40.png image41.png image42.png image43.png image44.jpg image45.jpg image46.jpg image47.png image48.jpg image49.png image50.png image51.png image60.png image61.png image52.jpg image53.jpg image54.png image55.png image56.png image57.png image58.png image59.png image62.jpg image63.jpg image64.png image65.png image66.png image67.png image68.png image69.png image70.png image71.png image72.jpg image73.jpg image74.jpg image75.jpg image81.png image82.png image83.png image84.png image85.png image86.png image87.png image76.png image77.png image78.png image79.png image80.png image88.png image89.jpg image91.png image90.png image97.png image98.png image92.png image93.png image94.png image95.png image96.png image99.png image100.png image101.png image102.png image103.png image104.png image105.png image106.png image107.png image108.png image109.png image110.png image111.png image112.png image113.png image114.png image115.png image116.png image117.png image118.png …

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prezentatsiya powerpoint muhammad al-xorazmiy nomidagi toshkent axborot texnologiyalari universiteti axborotlarni izlash vaajratib olish swd 1316 axborot izlashda klasterlash. axborot izlashda klasterlash, muammo bayonoti. ma’ruza rejasi hujjatlarning vektor fazo modeli klasterlash tushunchasi klasterlash usullari k-means klasterlash usuli k-means bo‘yicha amaliy masala hujjatlarning vektor fazo modeli tf (term frequency) – atama chastotasi idf (inverse document frequency)- hujjatning teskari chastotasi tf-idf (term frequency- inverse document frequency) – atamaning hujjatga tegishlilik ko’rsatkichi hujjatlarning vektor fazo modeli doc1= [tf1, tf2 ,…., tfn] doc2= [tf1, tf2 ,…., tfn] doc3= [tf1, tf2 ,…., tfn] doc4= [tf1, tf2 ,…., tfn] doc5= [tf1, tf2 ,…., tfn] doc6= [tf1, tf2 ,…., tfn] … do...

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