13 دى 1395, 11:53 ق.ظ
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14 دى 1395, 07:54 ب.ظ
(12 دى 1395 11:06 ق.ظ)soosoo نوشته شده توسط: [ -> ]Cluster refining-Do additional passes over the dataset
and reassign data points to the closest centroids from
above step.
Find the density attractors using Hill-Climbing approach
and they should be local maxima of overall
density function
من دیر دیر سر میزنم، الآن دیدم - بفرمایید:
پالایش خوشه: چند گذر دیگه روی مجموعه داده انجام بدید و نقاط داده رو به نزدیکترین مرکز ثقلهای گام بالایی باز-انتساب بدید.
جاذبهای چگالی را با استفاده از رهیافت تپهنوردی پیدا کنید؛ آنها بایستی بیشینهی محلی تابع چگالی کلی باشند.
15 دى 1395, 10:57 ق.ظ
یه دنیا ممنون
خیلی لطف کردید
میشه خواهش کنم اگه ممکنه ایمیل تون رو بدید( به صورت پیام خصوصی )تا اگه سوالی داشتم و شما نیامدید اینجا مزاحمتون بشم؟
ممنون میشم....
خیلی لطف کردید
میشه خواهش کنم اگه ممکنه ایمیل تون رو بدید( به صورت پیام خصوصی )تا اگه سوالی داشتم و شما نیامدید اینجا مزاحمتون بشم؟
ممنون میشم....
15 دى 1395, 08:33 ب.ظ
(15 دى 1395 10:57 ق.ظ)soosoo نوشته شده توسط: [ -> ]ایمیل تون رو بدید ...متأسفانه وقت فراغت کمی دارم؛ همینجا بپرسید، شاید دوستان زودتر جواب دادن، منم سعی میکنم بیشتر سر بزنم.
16 دى 1395, 10:25 ق.ظ
سلام
چشم
به هر حال ممنون از لطف تون
شرمندم
این یه مورد رو هم میشه کمکم کنید؟
It is descendant of CLIQUE algorithm in which instead of using a fixed size cell grid structure with an equal number of bins in each dimension, it constructs an adaptive grid to improve the quality of clustering. T he algorithm is as follows: •In a single pass an adaptive grid structure was constructed by considering set of all points. •Compute the histogram by reading blocks of data into memory using bins. •Bins are grouped together based on the dominance factor α. •Select the bins that are α times dense greater than average as p candidate dense units (CDU). •Recursively the process continuous to form new p-CDU’s and merge adjacent CDU’s into clusters.
ممنون میشم
ببخشید
چشم
به هر حال ممنون از لطف تون
شرمندم
این یه مورد رو هم میشه کمکم کنید؟
It is descendant of CLIQUE algorithm in which instead of using a fixed size cell grid structure with an equal number of bins in each dimension, it constructs an adaptive grid to improve the quality of clustering. T he algorithm is as follows: •In a single pass an adaptive grid structure was constructed by considering set of all points. •Compute the histogram by reading blocks of data into memory using bins. •Bins are grouped together based on the dominance factor α. •Select the bins that are α times dense greater than average as p candidate dense units (CDU). •Recursively the process continuous to form new p-CDU’s and merge adjacent CDU’s into clusters.
ممنون میشم
ببخشید
16 دى 1395, 07:23 ب.ظ
خدا خیرتون بده
این هم اگه ممکنه بی زحمت برای من ترجمه بفرمایید من هم گیر خوشه ها افتادم
•Attributes are strongly connected if the data points are having larger frequency. •Clusters are formed based on the co-occurrences of attribute value pairs. •A cluster is formed if any segment is having no of elements α times greater than elements of other.
--
•An attribute object P is selected and retrieves all objects densities whether they are reachable from P with respect to neighborhood of the object (NPred) and minimum weighted cardinality (Min weight). •If P is a core object this procedure yields a density connected set Ci with respect to NPred and Min weight. •Otherwise it does not belong to any density connected set Ci. T his procedure is iteratively applied to each object P which has not yet been classified.
--
•High amplitude signals are applied to the corresponding cluster interiors and high frequency is applied to f ind boundary of cluster. •Signals are applied to the attribute space in order to form cluster with more sharp and eliminates outliers easily.
--
The clusters are represented probabilistically by conditional probability P (A = v | C) with which attribute A has value v, given that the instance belongs to class C.
ببخشید زیاد شد خدا خیرتون بده..
این هم اگه ممکنه بی زحمت برای من ترجمه بفرمایید من هم گیر خوشه ها افتادم
•Attributes are strongly connected if the data points are having larger frequency. •Clusters are formed based on the co-occurrences of attribute value pairs. •A cluster is formed if any segment is having no of elements α times greater than elements of other.
--
•An attribute object P is selected and retrieves all objects densities whether they are reachable from P with respect to neighborhood of the object (NPred) and minimum weighted cardinality (Min weight). •If P is a core object this procedure yields a density connected set Ci with respect to NPred and Min weight. •Otherwise it does not belong to any density connected set Ci. T his procedure is iteratively applied to each object P which has not yet been classified.
--
•High amplitude signals are applied to the corresponding cluster interiors and high frequency is applied to f ind boundary of cluster. •Signals are applied to the attribute space in order to form cluster with more sharp and eliminates outliers easily.
--
The clusters are represented probabilistically by conditional probability P (A = v | C) with which attribute A has value v, given that the instance belongs to class C.
ببخشید زیاد شد خدا خیرتون بده..
19 دى 1395, 11:13 ق.ظ
سلام
آقای شریعتی
کجایید؟
کسی نیست راهنمایی کنه؟،؟
خواهش...
آقای شریعتی
کجایید؟
کسی نیست راهنمایی کنه؟،؟
خواهش...
21 دى 1395, 02:21 ب.ظ
(19 دى 1395 11:13 ق.ظ)soosoo نوشته شده توسط: [ -> ]سلاممادربزرگم فوت کرده و در شرایطی نبودم که بتونم به اینجا سر بزنم ...
آقای شریعتی
کجایید؟ ...
یک روش خوشهبندی منشق شده از مسئلهی Clique هست که از ساختار تورین (grid) پویا و ادغام مداوم ظرف(bin)های متراکم استفاده میکنه.
- سؤال موردی بپرسید، در حد یک خط. یا بگید که "من از این پاراگراف این رو فهمیدم، آیا درسته؟"
- متن فوق خلاصه و برای کسی هست که در این زمینه آشنایی کافی با رهیافتها و چالشها داره. اگه مثل من ناآشنا با این زمینه هستید شاید نیاز داشته باشید روش فوق رو کاملتر و از مقالهی اصلی بخونید تا درست متوجه بشید.
22 دى 1395, 12:34 ب.ظ
تسلیت میگم
ممنون
چشم
ممنون
چشم
30 دى 1395, 11:05 ق.ظ
سلام
کسی میدونه مفهوم این پاراگراف چیه؟؟
هرچی میخونمش و ترجمش میکنم متوجه اش نمیگم. ..
Fiduccia-Mattheyeses algorithm (FM algorithm hereafter) is another heuristic partitioning algorithm which generalizes the concept of swapping of nodes introduced in KL algorithm. To contrast FM algorithm with KL algorithm, FM algorithm is designed to work on hypergraphs and instead of swapping a pair of nodes as was happening in KL algorithm, FM algorithm swaps a single node in each iteration. The basic essence of FM algorithm is the same as KL algorithm – we define gains for each vertex of the (hyper)graph, select one node according to some criterion, remove it from its present partition and put it to the other partition, lock that vertex, update gains of all other unlocked vertices and iterate these steps until we reach a local optimum configuration. The tool hMETIS implements an augmented version of FM algorithm (please refer to Existing tools for Graph Partitioning section).
کسی میدونه مفهوم این پاراگراف چیه؟؟
هرچی میخونمش و ترجمش میکنم متوجه اش نمیگم. ..
Fiduccia-Mattheyeses algorithm (FM algorithm hereafter) is another heuristic partitioning algorithm which generalizes the concept of swapping of nodes introduced in KL algorithm. To contrast FM algorithm with KL algorithm, FM algorithm is designed to work on hypergraphs and instead of swapping a pair of nodes as was happening in KL algorithm, FM algorithm swaps a single node in each iteration. The basic essence of FM algorithm is the same as KL algorithm – we define gains for each vertex of the (hyper)graph, select one node according to some criterion, remove it from its present partition and put it to the other partition, lock that vertex, update gains of all other unlocked vertices and iterate these steps until we reach a local optimum configuration. The tool hMETIS implements an augmented version of FM algorithm (please refer to Existing tools for Graph Partitioning section).
01 بهمن 1395, 09:43 ق.ظ
کسی نیست کمک کنه
01 بهمن 1395, 08:29 ب.ظ
اینها چیزیه که من میفهمم:
در هر تکرار، تو KL یک جفت گره از دو پارتیشن (افراز) با هم جابهجا میشن ولی تو FM یک گره از افرازی به افراز دیگه منتقل میشه.
منظور از فرا-گراف اینه که داریم روی افرازهای موجود (یعنی در سطح افراز در مقابل در سطح گره) کار میکنیم. و به نظرم منظورش از رأس، یک افراز هست نه یک گره ساده.
در هر تکرار، تو KL یک جفت گره از دو پارتیشن (افراز) با هم جابهجا میشن ولی تو FM یک گره از افرازی به افراز دیگه منتقل میشه.
منظور از فرا-گراف اینه که داریم روی افرازهای موجود (یعنی در سطح افراز در مقابل در سطح گره) کار میکنیم. و به نظرم منظورش از رأس، یک افراز هست نه یک گره ساده.
(30 دى 1395 11:05 ق.ظ)soosoo نوشته شده توسط: [ -> ]we define gains for each vertex of the (hyper)graph, select one node according to some criterion, remove it from its present partition and put it to the other partition, lock that vertex, update gains of all other unlocked vertices and iterate these steps until we reach a local optimum configuration.بهرهای برای هر رأس از (فرا)گراف تعریف میکنیم، بر اساس یک معیار گرهای را انتخاب میکنیم، آن را از افراز جاری به افراز جدیدی میبریم، آن رأس را قفل میکنیم، بهرهی همهی رئوس قفل نشده را بهروزرسانی میکنیم، و این مراحل را تا زمانی که به پیکربندی بهینهی محلی برسیم ادامه میدهیم.
02 بهمن 1395, 01:58 ب.ظ
Thus, it is expected that microprocessor manufacturers
include decimal floating–point units in their products oriented to mainframe servers to satisfy the high performance demands of current financial, commercial and user–oriented applications
include decimal floating–point units in their products oriented to mainframe servers to satisfy the high performance demands of current financial, commercial and user–oriented applications
02 بهمن 1395, 03:34 ب.ظ
these partial products using a novel decimal carry–save
addition tree.
addition tree.
02 بهمن 1395, 07:47 ب.ظ