diff --git a/performance_clusters.tex b/performance_clusters.tex
index c2fc50a..7b58b5f 100644
--- a/performance_clusters.tex
+++ b/performance_clusters.tex
@@ -46,10 +46,10 @@ sensitivity of performance clusters to number of frequency settings available in
 the system.
 
 \subsection{Performance Clusters}
-We search for the performance clusters using an algorithm that is similar to the algorithm we used to find the optimal settings. We
-first filter the settings that fall within given inefficiency budget, and
-then search the optimal settings in the first pass. In the second pass, we find all of the
-settings that have speedup within the \textit{cluster threshold} of the optimal performance settings.
+We search for the performance clusters using an algorithm that is similar to the approach we used to find the optimal settings. We
+first filter the settings that fall within a given inefficiency budget, and
+then search for the optimal settings in the first pass. In the second pass, we find all of the
+settings that have a speedup within the specified \textit{cluster threshold} of the optimal performance.
 
 \begin{figure*}[t]
     \begin{subfigure}[t]{\textwidth}
@@ -87,7 +87,7 @@ our benchmarks, we observed that the maximum achievable inefficiency is anywhere
 chose inefficiency budgets of 1 and 1.3 to cover low and mid inefficiency
 budgets. %, as energy distribution among components becomes critical to extract best performance.
 Cluster thresholds of 1\% and
-5\% allow us to model the two extremes of performance degradation bounds.
+5\% allow us to model the two extremes of tolerable performance degradation bounds.
 A cluster threshold of less than 1\% may limit the ability to tune less often.
 While cluster thresholds greater than 5\% are probably not realistic as user is already
 compromising performance by setting low inefficiency budgets to save energy.
@@ -121,7 +121,7 @@ Not all of the stable regions increase in length with increasing inefficiency bu
 %Increase in the length of stable regions with increase in
 %inefficiency is a 
 %function of workload characteristics. 
-If the consecutive
+If consecutive
 samples of a workload have a small difference in performance but differ significantly in energy
 consumption then only at
 higher inefficiency budgets will the system find common settings for these
@@ -129,8 +129,8 @@ consecutive samples. % because all settings under an inefficiency budget are con
 %Note that we find the performance clusters by considering
 %the settings that fall under a given inefficiency budget. 
 This is because,
-performance clusters of higher inefficiencies can include settings operating at
-lower inefficiencies as long as their performance degradation is within set
+the performance clusters of higher inefficiencies can include settings operating at
+lower inefficiencies as long as their performance degradation is within the
 cluster threshold. For example, the memory frequency oscillates for samples
 32-39 for \textit{gobmk}
 at inefficiency budget of 1.0, while the system could stay fixed at 800MHz memory at inefficiency