My goals
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Ebony's goals for term 2 include writing more pages in stories to improve her literacy and moving up a level in numeracy by learning to count more. She outlined separate goals for literacy and numeracy, focusing on increasing the length and complexity of her writing and counting skills.
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My goal for term two
My goals for term two include improving my literacy skills like reading better, getting better at numeracy such as spelling numbers correctly, and singing to work on personal skills, with being nice as my social goal.
Clustering
Clustering has been one of the most widely studied topics
in data mining and k-means clustering has been one of
the popular clustering algorithms. K-means requires several
passes on the entire dataset, which can make it very expensive
for large disk-resident datasets. In view of this, a lot of work
has been done on various approximate versions of k-means,
which require only one or a small number of passes on the
entire dataset.
In this paper, we present a new algorithm, called Fast and
Exact K-means Clustering (FEKM), which typically requires
only one or a small number of passes on the entire dataset,
and provably produces the same cluster centers as reported
by the original k-means algorithm. The algorithm uses sampling
to create initial cluster centers, and then takes one or
more passes over the entire dataset to adjust these cluster
centers. We provide theoretical analysis to show that the cluster
centers thus reported are the same as the ones computed
by the original k-means algorithm. Experimental results from
a number of real and synthetic datasets show speedup between
a factor of 2 and 4.5, as compared to k-means.
This paper also describes and evaluates a distributed version
of FEKM, which we refer to as DFEKM. This algorithm
is suitable for analyzing data that is distributed across loosely
coupled machines. Unlike the previous work in this area,
DFEKM provably produces the same results as the original
k-means algorithm. Our experimental results show that
DFEKM is clearly better than two other possible options for
exact clustering on distributed data, which are down-loading
all data and running sequential k-means, or running parallel
k-means on a loosely coupled configuration. Moreover, even
in a tightly coupled environment, DFEKM can outperform
parallel k-means if there is a significant load imbalance
Presentation1
Isaac's goals for term 2 include improving his writing skills in literacy, learning how to do harder two-digit subtraction problems in numeracy, riding his BMX bike more for personal development, and making more friends to develop socially.
N athan term 2 goals
Nathan's goals for Term 2 include improving his literacy by focusing on his work without distractions, advancing his numeracy through more word problems, playing more hockey for personal growth, and being friendlier with others socially.
Goals term 2
My term 2 goals are to write more without distraction in literacy, skip count by 7s and 3s in numeracy, ride my bike without crashing for personal development, and be more helpful socially.
Goals term 2
My term 2 goals are to write more without distraction in literacy, skip count by 7s and 3s in numeracy, ride my bike without crashing for personal development, and be more helpful socially.
Recommended
My goal for term two
My goals for term two include improving my literacy skills like reading better, getting better at numeracy such as spelling numbers correctly, and singing to work on personal skills, with being nice as my social goal.
Clustering
Clustering has been one of the most widely studied topics
in data mining and k-means clustering has been one of
the popular clustering algorithms. K-means requires several
passes on the entire dataset, which can make it very expensive
for large disk-resident datasets. In view of this, a lot of work
has been done on various approximate versions of k-means,
which require only one or a small number of passes on the
entire dataset.
In this paper, we present a new algorithm, called Fast and
Exact K-means Clustering (FEKM), which typically requires
only one or a small number of passes on the entire dataset,
and provably produces the same cluster centers as reported
by the original k-means algorithm. The algorithm uses sampling
to create initial cluster centers, and then takes one or
more passes over the entire dataset to adjust these cluster
centers. We provide theoretical analysis to show that the cluster
centers thus reported are the same as the ones computed
by the original k-means algorithm. Experimental results from
a number of real and synthetic datasets show speedup between
a factor of 2 and 4.5, as compared to k-means.
This paper also describes and evaluates a distributed version
of FEKM, which we refer to as DFEKM. This algorithm
is suitable for analyzing data that is distributed across loosely
coupled machines. Unlike the previous work in this area,
DFEKM provably produces the same results as the original
k-means algorithm. Our experimental results show that
DFEKM is clearly better than two other possible options for
exact clustering on distributed data, which are down-loading
all data and running sequential k-means, or running parallel
k-means on a loosely coupled configuration. Moreover, even
in a tightly coupled environment, DFEKM can outperform
parallel k-means if there is a significant load imbalance
Presentation1
Isaac's goals for term 2 include improving his writing skills in literacy, learning how to do harder two-digit subtraction problems in numeracy, riding his BMX bike more for personal development, and making more friends to develop socially.
N athan term 2 goals
Nathan's goals for Term 2 include improving his literacy by focusing on his work without distractions, advancing his numeracy through more word problems, playing more hockey for personal growth, and being friendlier with others socially.
Goals term 2
My term 2 goals are to write more without distraction in literacy, skip count by 7s and 3s in numeracy, ride my bike without crashing for personal development, and be more helpful socially.
Goals term 2
My term 2 goals are to write more without distraction in literacy, skip count by 7s and 3s in numeracy, ride my bike without crashing for personal development, and be more helpful socially.
Term two goals
Holli's term two goals are to read three pages in her books for literacy, improve adding groups of numbers and telling time for numeracy, learn piano and computers skills for personal development, and make more friends and be nicer to others for social goals.
Easter island
Easter Island is known for over 600 giant stone statues carved over 1,000 years ago, though it is unclear if coconuts, chocolate or the Easter Bunny are present on the island today according to a document by Ellie.
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Brianna has set several goals for the second term including taking 2-3 books home to read to improve her literacy, improving her spelling skills, writing more pages in a smaller and neater style. For numeracy, her goal is to learn more adding. Her personal goal is to learn more on the computer at school and her social goal is to try not to fight with her sister and cousins.
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Easter Island is known for over 600 giant stone statues carved over 1,000 years ago, though it is unclear if coconuts, chocolate or the Easter Bunny are present on the island today according to a document by Ellie.
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Easter Island is home to over 600 giant stone statues carved over 1,000 years ago in the Pacific Ocean. The island is also known for questions about whether it contains coconuts, chocolate, people, and if the Easter Bunny is real.
My goal for term two
My goals for term two include improving my literacy skills like reading better, getting better at numeracy such as spelling numbers correctly, and singing to work on personal skills, with being nice as my social goal.
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My goals
- 1. My goals for term 2
- 2. Literacy My goal for literacy is to write more pages in stories.
- 3. Numeracy My goal for numeracy is to move up a level and count more.
- 6. By Ebony